The Application of BP Neural Network principal component analysis in the Forecasting the Road Traffic Accident
|
|
- Marshall Noel Thompson
- 5 years ago
- Views:
Transcription
1 ICTCT Extra Workshop, Bejng Proceedngs The Appcaton of BP Neura Network prncpa component anayss n Forecastng Road Traffc Accdent He Mng, GuoXucheng &LuGuangmng Transportaton Coege of Souast Unversty 07 room,yfu Budng, Souast Unversty Nanjng, Chna Phone: (0) Fax: E-ma: hemng9302@63.com, seuguo@63.com, gm0926@sohu.com Abstract Accordng to compexty and comprehensbty of factors whch affect road traffc safety, we use method of prncpa component anayss to refne new factors whch are neary ndependent, n we forecast road traffc accdent accordng to prncpa component by BP neura network smuaton, anayse reatonshp between traffc accdent evauatng ndex and causes of traffc accdent, ncudng peope, vehces, road and envronment. At ast we apped method to a case, from smuated resut we can nfer that method of BP neura network smuaton prncpa component anayss s superor to mutnoma fttng and BP neura network smuaton n effcency and precson. Key words: road traffc accdent, forecastng, BP neura network, prncpa component anayss.introducton In pannng of road safety, traffc forecastng s one of most basc tasks n pannng process, and t s most mportant ssue. Understandng future traffc accdent Scentfcay and accuratey s of great sgnfcance for overa grasp of road safety n order to prefabrcate correspondng measures. Exstng traffc forecastng methods can be grouped nto three genera categores[]: Frst extrapoaton method, that s, y use past to predct future state of nformaton, such as tme seres; Second, causaty, that s, based on avaabe nformaton, to dentfy reatonshp between varabes to predct future state, such as regresson anayss; Thrd, judge anayss, Experts predct future state rey on past experence and abty of comprehensve anayss methods. Athough forecastng methods have r advantages, but due to compex nature of transport system and dversty of traffc accdents, most do not ft data exst very we, extrapoaton s not enough, and forecastng resuts may devate from actua resuts and so on. Such as tme seres predcton, t uses ongtudna data of number of traffc accdents n past to predct ts movements over tme. The process does not nvove any or reevant factors. Athough regresson anayss can forecast accordng to transverse and ongtudna data, but t estabshes regresson equaton usng just some hstorca data, regresson equaton often consders ony part of affectng factors. Therefore, mode s not accurate enough. Judge anayss s quatatve, t based on subjectve experence, so forecastng may not accurate. POSTER SESSION 305
2 ICTCT Extra Workshop, Bejng Proceedngs In recent years, rapd deveopment of computer and artfca ntegence technoogy provde traffc modeng and forecastng wth new methods. Artfca neura network s composed of neurons wth dfferent functons, t can be used to smuate, operate and reason compcated nonnear system through neura network nteracton. It has extensve adaptng abty, earnng abty, mappng abty, and can approach to any nonnear functon n ory. In mutvarabe nonnear system modeng, t has made remarkabe achevements. BP neura network structure s ntutve, and t s most wdey used neura network. Whe Usng BP neura network system to smuate, frst s to dentfy factors. Traffc s very compex, we often adapt quatatve anayss to fnd a accdent factors so as to avod major factor be mssed.however, when nput varabes are too many, t w obvousy add to compexty of network, reduce network performance, greaty ncrease cacuaton of operatng tme, and decrease precson. To sove probem of too many nput varabes, ths paper proposes use of BP neura network traffc forecastng mode combned wth prncpa component anayss decreases orgna nput varabes through prncpa component anayss, obtans neary ndependent new factors whch ncude nformaton of orgna nput varabes. Then t uses se new factors as nput varabes so as to smpy nput varabes. Fnay, paper uses actua traffc data for traffc forecastng. 2.Traffc forecastng mode based on BP Neura Network prncpa component anayss 2. The structure and prncpe of BP Neura Network BP Neura Network s a one-way transmsson to mut-forward network, and except for nput and output nodes, re are aso one or more ayers of hdden nodes, nodes of same ayer s out of coupe wth each or, nput sgna passes from nput ayer nodes to hdden nodes foowed by transfer functon, n spread to output nodes. The output of each node ony nfuenced next output nodes, as shown n fgure : Fgure structure of BP Neura Network BP Neura networks can be vewed as a hghy nonnear mappng from nput to output n m n m namey F R : R,f(x)->Y For pattern: nput x R output y R g ( x ) = y (=, 2, n) The neura network s approxmate to compex functon after a number of smpe nonnear functons, and t can obtan output usng nput at w. 306 POSTER SESSION
3 ICTCT Extra Workshop, Bejng Proceedngs.transfer functon often s 0 S functon g(x)= x + e 2.error functon The pth pattern error computng formua E p = ( t p O p 2 ) 2 t p, O p are expected output and network s computng output. Through correctng weghts of network w j, T j and threshod θ,to make error functon E descend foowng drecton of mnmum oca gradent [3],[5] BP network nodes ncude: nput nodes x j, hdden nodes of network between nput nodes and hdden nodes s 2 y, output nodes O. The weght w j, weght of network between T hdden nodes and output nodes s. When desred output of output nodes s t, cacuaton formua of BP mode s: 3. formua of output O of output nodes: nput of nput nodes: x output of hdden nodes: output of output nodes connectng weght s j w j nodes threshod 4. output ayer s correctng formua: desred output of output nodes: t w x θ j j 3 j O = f Tj y θ 4 y =f O a patterns error: one pattern s error: e k = P e k k= E= p s number of patterns n s number of output nodes. error formua: correctng weght: δ = k s number of number of teratons. correcton of threshod 5. correcton formua of hdden node: error formua correcton formua of weght correcton of threshod n = <ε 5 ( k ) ( k ) t + O 6 t O * O * O 7 T ( k+ ) ( k ) = T + ηδ y ( k+ ) ( k ) θ = θ + η' δ ' δ ' = y ( y ) δ T 0 w ( k + ) j = w ( k ) j +η' δ ' x ( k+ ) ( k ) θ = θ + η' δ ' j POSTER SESSION 307
4 ICTCT Extra Workshop, Bejng 2.2 Traffc accdent forecastng mode Proceedngs 2.2. The major nfuencng factors of accdents Traffc accdents happen because of co-ordnaton of varous factors such as cars, roads, cmate and envronment. In consderaton of many factors that mpact on traffc, we seected popuaton, drvers, popuaton of arge vehces, popuaton of sma cars, meage of artera road, meage of mnor artera roads, ran and snow as seven factors Traffc accdent forecastng mode Prncpa component anayss Prncpa component anayss s use of dmenson reducton by constructng approprate near combnaton of orgna ndex, to produce a seres of uncorreated comprehensve ndexes, and to seect some of se ndexes, whch ncude as much nformaton as od ndexes, so as to use se new ndexes to refect ndvdua. Because method reduces dmenson by emnatng correaton between ndexes, t has been brngng n concern n recent years and becomng a unque mut-evauaton of technca ndexes. Based on anayss of man nfuencng factors of accdent, paper seects seven factors of accdent are x, x2, x3, x4, x5, x6, x7, adopts prncpa component anayss frst, and anayses se seven factors, as foows: Step Normazaton of factors Because every ndex has dfferent concept of magntude, before anayzng prncpa component, we need to normaze data, makng vaues range from 0 to. The method s as foows: ' x x = 3 max( x ) Step 2: Usng standardzed data to cacuate correaton matrx n R = ( r j ) 7 7 ( rj = n = x ' x ' j,,j=,2 7) 4 Step 3: Cacuate egenvaue and egenvector of correaton matrx R to get prncpa component Make R λ I = 0 cacuate 7 egenvaue such as λ (=,2 7), y are varances of prncpa components, rank m from sma to arge: λ λ2... λ7 0,so expresson of prncpa component s Y = X X X... X 5 ' = =,2, Step 4 Seect m prncpa components to make sure varance contrbuton rates a = m = 7 λ / λ > = 308 POSTER SESSION
5 ICTCT Extra Workshop, Bejng Proceedngs Through step to 4,we can cacuate r prncpa components are Y, Y2,... Ym m<7,and Y (=,2,,m) s near combnatons wth x ' (=,2,,7) and make Y, Y2,... Ym as nput of BP Neura Network to get forecastng resuts by study of BP network agorthm BP Neura Network smuaton usng prncpa components as nput factors The ory has been proved that three-ter Network system s a better mode for nonnear modeng, every contnuous functon can be reazed through one three-ayer neura network. In neura network forecastng mode, we use a three-ter network. That s, one nput ayer, one hdden ayer, one output ayer. Because network s abty to express s ncreasng wth number of nput ayer and output ayer ncreasng, and aso convergence rate s ncreasng, so mode s heoretca workabe. In condton of consderng factors of traffc accdent, paper uses m prncpa components as nput ayer, nput ayer has 7 neurons, hdden ayer has 0 neurons, output ayer has neurons. The output ayer s object varabe, namey number of traffc accdents. Iteraton process of BP neura network s as foows:. Gve nta vaue for weght coeffcent w j of a ayers. 2.Get a nodes output accordng to Get error e k accordng to 6,f t meets 5,or go to step 4. ' 4.Get errors( δ, δ ) of prncpa components and hdden ayers accordng to formua (7) and (0), n correct weghts accordng to 8 and,n go to step 2. (2-4 s teratve process). 3. Exampe Take a cty as an exampe, descrbe method of paper, data can be seen from tabe : year popuaton ten thousand drvers (ten thousand Tabe The traffc accdent data of a cty number of arge vehce number of sma vehce meage of artera road (km meage of mnor artera road km ran or snow d The number of traffc accdent POSTER SESSION 309
6 ICTCT Extra Workshop, Bejng Proceedngs 3. Data processng The factors of accdents ncude popuaton, drvers, popuaton of arge vehces, popuaton of sma cars, meage of artera road, meage of mnor artera roads, ran and snow, namey x, x2, x3, x4, x5, x6, x7, change of accdents wth years s as fgure 2. Fgure 2 change of accdents wth years Fgure 3 change of accdents wth years after normazaton Because dfference between factors s too arge, we normaze m accordng to data of 2006, as can been seen from fgure 3 and tabe 2. year popuaton ten thousand Tabe 2 The traffc accdent data after normazaton drvers (ten thousand number of arge vehce number of sma vehce meage of artera road (km meage of mnor artera road km ran or snow d The number of traffc accdent POSTER SESSION
7 ICTCT Extra Workshop, Bejng Proceedngs 3.2 Prncpa component anayss Do prncpa component anayss, get 3 prncpa components n condton of varance contrbuton rates s over 0.995, as can been seen from tabe 3: Tabe 3 The new components factors factor factor 2 factor 3 x x x x x x x contrbuton rate 99.07% 0.63% 0.27% 3.3 BP Neura Network combnng wth prncpa component anayss Take factor as nput and traffc accdent afer normazaton as output, we set up threeayer neura network mode, nput ayer has 7 neurons, hdden ayer has 0 neurons, output ayer has neurons. The process of smuaton can been seen as fgure 3,n order to make precson s hgh enough(<0e-4), resut s ,namey number of traffc accdent s If put prncpa component of years from 997 to 2005, resut s [ ].( comparson between smuaton resut and actua data can been seen from fgure 4) Fgure 3 resut of BP Neura NetworkFgure 4 comparson between combnng wth prncpa component anayss smuaton resut and actua data 3.4 The comparson of resuts Compare resut of BP Neura Network combnng wth prncpa component anayss, resut of BP Neura Network and resut of nonnear regresson by puttng factor as ndependent varabe,we fnd resut of BP Neura Network combnng wth prncpa component anayss s better than one of BP Neura Network and nonnear regresson. POSTER SESSION 3
8 ICTCT Extra Workshop, Bejng Proceedngs Tabe 4 comparson of resuts year actua data nonnear regresson BP Neura Network BP Neura Network combnng wth prncpa component anayss year Actua data nonnear regresson BP Neura Network BP Neura Network combnng wth prncpa component anayss The resut ndcates that BP Neura Network combnng wth prncpa component anayss s better than BP Neura Network n effcency and precson [4][6]. BP Neura Network combnng wth prncpa component anayss s very practcabe. 4.Summary We can concude from paper: The mert of mode:.the mode of BP Neura Network combnng wth prncpa component anayss s feasbe, resut s better than BP Neura Network and regresson. 2.The mode can consder a factors affectng traffc accdents, through prncpa component anayss, we get ess new factor as nput, t s better than BP Neura Network n effcency and precson. But se are aso some shortages:.the ory about stabty of BP Neura Network s fautness, we can ony try to cacuate hdden ayers and nodes, and method s mted n use. 2.The mode s same wth forecastng traffc accdents n stabe condton, not consderng change of exteror condtons, such as change of transport pocy and so on,ths s aso an mportant factor of traffc accdent. In a word, The mode of BP Neura Network combnng wth prncpa component anayss s feasbe,consderng compexty and randomcty of urban transportaton, appy range coud be deveoped except accdent forecastng, and mode needs to studed and mproved. 32 POSTER SESSION
9 ICTCT Extra Workshop, Bejng Reference Proceedngs Wang We.Research on Sustanabe Deveopment Pannng Theory of Urban Transportaton [J].Journa of souast unversty,200,3:(:6) Awtan, Modeng of Matera Behavor Data n a Functona Forms Utabe for Neura Network Representaton[J].Computtona Materas Scence,999,5: Martn E W,Defferyes D W,Hoffer J A eta. Managng nformaton technoogy. New York:MacMan,99 Su Jnmng ZhangLanhua LuBo. The Appy of Matab. BeJng: The Chna Eectronc Industry Pubshng House, H.I.CHOI.F Abrcaton of Hgh Conductvty Copper Aoys by Rod Mng. Journa of Materas Scence Letters,997,6: I.A.Basheer.Artfca, neura network: Fundamentas, Computng, Desgn, and Appcaton. Journa of Mcroboogca Methods,2000,43:33 POSTER SESSION 33
Neural network-based athletics performance prediction optimization model applied research
Avaabe onne www.jocpr.com Journa of Chemca and Pharmaceutca Research, 04, 6(6):8-5 Research Artce ISSN : 0975-784 CODEN(USA) : JCPRC5 Neura networ-based athetcs performance predcton optmzaton mode apped
More informationResearch on Complex Networks Control Based on Fuzzy Integral Sliding Theory
Advanced Scence and Technoogy Letters Vo.83 (ISA 205), pp.60-65 http://dx.do.org/0.4257/ast.205.83.2 Research on Compex etworks Contro Based on Fuzzy Integra Sdng Theory Dongsheng Yang, Bngqng L, 2, He
More informationPredicting Model of Traffic Volume Based on Grey-Markov
Vo. No. Modern Apped Scence Predctng Mode of Traffc Voume Based on Grey-Marov Ynpeng Zhang Zhengzhou Muncpa Engneerng Desgn & Research Insttute Zhengzhou 5005 Chna Abstract Grey-marov forecastng mode of
More informationShort-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques
Energes 20, 4, 73-84; do:0.3390/en40073 Artce OPEN ACCESS energes ISSN 996-073 www.mdp.com/journa/energes Short-Term Load Forecastng for Eectrc Power Systems Usng the PSO-SVR and FCM Custerng Technques
More informationNONLINEAR SYSTEM IDENTIFICATION BASE ON FW-LSSVM
Journa of heoretca and Apped Informaton echnoogy th February 3. Vo. 48 No. 5-3 JAI & LLS. A rghts reserved. ISSN: 99-8645 www.jatt.org E-ISSN: 87-395 NONLINEAR SYSEM IDENIFICAION BASE ON FW-LSSVM, XIANFANG
More informationAssociative Memories
Assocatve Memores We consder now modes for unsupervsed earnng probems, caed auto-assocaton probems. Assocaton s the task of mappng patterns to patterns. In an assocatve memory the stmuus of an ncompete
More informationIDENTIFICATION OF NONLINEAR SYSTEM VIA SVR OPTIMIZED BY PARTICLE SWARM ALGORITHM
Journa of Theoretca and Apped Informaton Technoogy th February 3. Vo. 48 No. 5-3 JATIT & LLS. A rghts reserved. ISSN: 99-8645 www.att.org E-ISSN: 87-395 IDENTIFICATION OF NONLINEAR SYSTEM VIA SVR OPTIMIZED
More informationSupplementary Material: Learning Structured Weight Uncertainty in Bayesian Neural Networks
Shengyang Sun, Changyou Chen, Lawrence Carn Suppementary Matera: Learnng Structured Weght Uncertanty n Bayesan Neura Networks Shengyang Sun Changyou Chen Lawrence Carn Tsnghua Unversty Duke Unversty Duke
More informationA principal component analysis using SPSS for Multi-objective Decision Location Allocation Problem
Zpeng Zhang A prncpa component anayss usng SPSS for Mut-objectve Decson Locaton Aocaton Probem ZIPENG ZHANG Schoo of Management Scence and Engneerng Shandong Norma Unversty No.88 Cuture Rode, Jnan Cty,
More informationImage Classification Using EM And JE algorithms
Machne earnng project report Fa, 2 Xaojn Sh, jennfer@soe Image Cassfcaton Usng EM And JE agorthms Xaojn Sh Department of Computer Engneerng, Unversty of Caforna, Santa Cruz, CA, 9564 jennfer@soe.ucsc.edu
More information3. Stress-strain relationships of a composite layer
OM PO I O U P U N I V I Y O F W N ompostes ourse 8-9 Unversty of wente ng. &ech... tress-stran reatonshps of a composte ayer - Laurent Warnet & emo Aerman.. tress-stran reatonshps of a composte ayer Introducton
More informationMultispectral Remote Sensing Image Classification Algorithm Based on Rough Set Theory
Proceedngs of the 2009 IEEE Internatona Conference on Systems Man and Cybernetcs San Antono TX USA - October 2009 Mutspectra Remote Sensng Image Cassfcaton Agorthm Based on Rough Set Theory Yng Wang Xaoyun
More informationA finite difference method for heat equation in the unbounded domain
Internatona Conerence on Advanced ectronc Scence and Technoogy (AST 6) A nte derence method or heat equaton n the unbounded doman a Quan Zheng and Xn Zhao Coege o Scence North Chna nversty o Technoogy
More informationNetworked Cooperative Distributed Model Predictive Control Based on State Observer
Apped Mathematcs, 6, 7, 48-64 ubshed Onne June 6 n ScRes. http://www.scrp.org/journa/am http://dx.do.org/.436/am.6.73 Networed Cooperatve Dstrbuted Mode redctve Contro Based on State Observer Ba Su, Yanan
More informationCOXREG. Estimation (1)
COXREG Cox (972) frst suggested the modes n whch factors reated to fetme have a mutpcatve effect on the hazard functon. These modes are caed proportona hazards (PH) modes. Under the proportona hazards
More informationDevelopment of whole CORe Thermal Hydraulic analysis code CORTH Pan JunJie, Tang QiFen, Chai XiaoMing, Lu Wei, Liu Dong
Deveopment of whoe CORe Therma Hydrauc anayss code CORTH Pan JunJe, Tang QFen, Cha XaoMng, Lu We, Lu Dong cence and technoogy on reactor system desgn technoogy, Nucear Power Insttute of Chna, Chengdu,
More informationMARKOV CHAIN AND HIDDEN MARKOV MODEL
MARKOV CHAIN AND HIDDEN MARKOV MODEL JIAN ZHANG JIANZHAN@STAT.PURDUE.EDU Markov chan and hdden Markov mode are probaby the smpest modes whch can be used to mode sequenta data,.e. data sampes whch are not
More informationERROR MODELING FOR STRUCTURAL DEFORMATIONS OF MULTI-AXIS SYSTEM BASED ON SVR
Journa of Theoretca and Apped Informaton Technoogy 3 st January 03. Vo. 47 No.3 005-03 JATIT & LLS. A rghts reserved. ISSN: 99-8645 www.jatt.org E-ISSN: 87-395 ERROR MODELING FOR STRUCTURAL DEFORMATIONS
More informationSupport Vector Machine Technique for Wind Speed Prediction
Internatona Proceedngs of Chemca, Boogca and Envronmenta Engneerng, Vo. 93 (016) DOI: 10.7763/IPCBEE. 016. V93. Support Vector Machne Technque for Wnd Speed Predcton Yusuf S. Turkan 1 and Hacer Yumurtacı
More informationAdaptive and Iterative Least Squares Support Vector Regression Based on Quadratic Renyi Entropy
daptve and Iteratve Least Squares Support Vector Regresson Based on Quadratc Ren Entrop Jngqng Jang, Chu Song, Haan Zhao, Chunguo u,3 and Yanchun Lang Coege of Mathematcs and Computer Scence, Inner Mongoa
More informationIV. Performance Optimization
IV. Performance Optmzaton A. Steepest descent algorthm defnton how to set up bounds on learnng rate mnmzaton n a lne (varyng learnng rate) momentum learnng examples B. Newton s method defnton Gauss-Newton
More informationA General Column Generation Algorithm Applied to System Reliability Optimization Problems
A Genera Coumn Generaton Agorthm Apped to System Reabty Optmzaton Probems Lea Za, Davd W. Cot, Department of Industra and Systems Engneerng, Rutgers Unversty, Pscataway, J 08854, USA Abstract A genera
More informationLecture Notes on Linear Regression
Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume
More informationSparse Training Procedure for Kernel Neuron *
Sparse ranng Procedure for Kerne Neuron * Janhua XU, Xuegong ZHANG and Yanda LI Schoo of Mathematca and Computer Scence, Nanng Norma Unversty, Nanng 0097, Jangsu Provnce, Chna xuanhua@ema.nnu.edu.cn Department
More informationXin Li Department of Information Systems, College of Business, City University of Hong Kong, Hong Kong, CHINA
RESEARCH ARTICLE MOELING FIXE OS BETTING FOR FUTURE EVENT PREICTION Weyun Chen eartment of Educatona Informaton Technoogy, Facuty of Educaton, East Chna Norma Unversty, Shangha, CHINA {weyun.chen@qq.com}
More informationD hh ν. Four-body charm semileptonic decay. Jim Wiss University of Illinois
Four-body charm semeptonc decay Jm Wss Unversty of Inos D hh ν 1 1. ector domnance. Expected decay ntensty 3. SU(3) apped to D s φν 4. Anaytc forms for form factors 5. Non-parametrc form factors 6. Future
More informationNested case-control and case-cohort studies
Outne: Nested case-contro and case-cohort studes Ørnuf Borgan Department of Mathematcs Unversty of Oso NORBIS course Unversty of Oso 4-8 December 217 1 Radaton and breast cancer data Nested case contro
More informationSensitivity Analysis Using Neural Network for Estimating Aircraft Stability and Control Derivatives
Internatona Conference on Integent and Advanced Systems 27 Senstvty Anayss Usng Neura Networ for Estmatng Arcraft Stabty and Contro Dervatves Roht Garhwa a, Abhshe Hader b and Dr. Manoranan Snha c Department
More informationBoundary Value Problems. Lecture Objectives. Ch. 27
Boundar Vaue Probes Ch. 7 Lecture Obectves o understand the dfference between an nta vaue and boundar vaue ODE o be abe to understand when and how to app the shootng ethod and FD ethod. o understand what
More informationAdaptive LRBP Using Learning Automata for Neural Networks
Adaptve LRBP Usng Learnng Automata for eura etworks *B. MASHOUFI, *MOHAMMAD B. MEHAJ (#, *SAYED A. MOTAMEDI and **MOHAMMAD R. MEYBODI *Eectrca Engneerng Department **Computer Engneerng Department Amrkabr
More informationLINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity
LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have
More informationNegative Binomial Regression
STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...
More informationk p theory for bulk semiconductors
p theory for bu seconductors The attce perodc ndependent partce wave equaton s gven by p + V r + V p + δ H rψ ( r ) = εψ ( r ) (A) 4c In Eq. (A) V ( r ) s the effectve attce perodc potenta caused by the
More informationOptimum Selection Combining for M-QAM on Fading Channels
Optmum Seecton Combnng for M-QAM on Fadng Channes M. Surendra Raju, Ramesh Annavajjaa and A. Chockangam Insca Semconductors Inda Pvt. Ltd, Bangaore-56000, Inda Department of ECE, Unversty of Caforna, San
More informationA principal component analysis and entropy value calculate method in SPSS for MDLAP model
A prncpa component anayss and entropy vaue cacuate method n SPSS for MDLAP mode ZIPENG ZHANG Schoo of Management scence and Engneerng, Shandong Norma Unversty, Jnan, Chna, HONGGUO WANG Schoo of nformaton
More informationLinear Approximation with Regularization and Moving Least Squares
Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...
More informationApplication of support vector machine in health monitoring of plate structures
Appcaton of support vector machne n heath montorng of pate structures *Satsh Satpa 1), Yogesh Khandare ), Sauvk Banerjee 3) and Anrban Guha 4) 1), ), 4) Department of Mechanca Engneerng, Indan Insttute
More informationMULTIVARIABLE FUZZY CONTROL WITH ITS APPLICATIONS IN MULTI EVAPORATOR REFRIGERATION SYSTEMS
MULTIVARIABLE FUZZY CONTROL WITH I APPLICATIONS IN MULTI EVAPORATOR REFRIGERATION SYSTEMS LIAO QIANFANG Schoo of Eectrca and Eectronc Engneerng A thess submtted to the Nanyang Technoogca Unversty n parta
More informationNUMERICAL DIFFERENTIATION
NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the
More informationMODEL TUNING WITH THE USE OF HEURISTIC-FREE GMDH (GROUP METHOD OF DATA HANDLING) NETWORKS
MODEL TUNING WITH THE USE OF HEURISTIC-FREE (GROUP METHOD OF DATA HANDLING) NETWORKS M.C. Schrver (), E.J.H. Kerchoffs (), P.J. Water (), K.D. Saman () () Rswaterstaat Drecte Zeeand () Deft Unversty of
More informationOptimization of JK Flip Flop Layout with Minimal Average Power of Consumption based on ACOR, Fuzzy-ACOR, GA, and Fuzzy-GA
Journa of mathematcs and computer Scence 4 (05) - 5 Optmzaton of JK Fp Fop Layout wth Mnma Average Power of Consumpton based on ACOR, Fuzzy-ACOR, GA, and Fuzzy-GA Farshd Kevanan *,, A Yekta *,, Nasser
More informationQuantitative Evaluation Method of Each Generation Margin for Power System Planning
1 Quanttatve Evauaton Method of Each Generaton Margn for Poer System Pannng SU Su Department of Eectrca Engneerng, Tohoku Unversty, Japan moden25@ececetohokuacjp Abstract Increasng effcency of poer pants
More informationApplication of Particle Swarm Optimization to Economic Dispatch Problem: Advantages and Disadvantages
Appcaton of Partce Swarm Optmzaton to Economc Dspatch Probem: Advantages and Dsadvantages Kwang Y. Lee, Feow, IEEE, and Jong-Bae Par, Member, IEEE Abstract--Ths paper summarzes the state-of-art partce
More informationSupervised Learning. Neural Networks and Back-Propagation Learning. Credit Assignment Problem. Feedforward Network. Adaptive System.
Part 7: Neura Networ & earnng /2/05 Superved earnng Neura Networ and Bac-Propagaton earnng Produce dered output for tranng nput Generaze reaonaby & appropratey to other nput Good exampe: pattern recognton
More informationChapter 13: Multiple Regression
Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to
More informationThe Leak Detection of Heating Pipe Based on Multi-Scale Correlation Algorithm of Wavelet
Sensors & Transducers Vo. 5 Speca Issue December 03 pp. 80-88 Sensors & Transducers 03 by IFSA http://www.sensorsporta.com The Lea Detecton of Heatng Ppe Based on ut-scae Correaton Agorthm of Waeet Xufang
More informationA Fast Computer Aided Design Method for Filters
2017 Asa-Pacfc Engneerng and Technology Conference (APETC 2017) ISBN: 978-1-60595-443-1 A Fast Computer Aded Desgn Method for Flters Gang L ABSTRACT *Ths paper presents a fast computer aded desgn method
More informationA new P system with hybrid MDE- k -means algorithm for data. clustering. 1 Introduction
Wesun, Lasheng Xang, Xyu Lu A new P system wth hybrd MDE- agorthm for data custerng WEISUN, LAISHENG XIANG, XIYU LIU Schoo of Management Scence and Engneerng Shandong Norma Unversty Jnan, Shandong CHINA
More informationCyclic Codes BCH Codes
Cycc Codes BCH Codes Gaos Feds GF m A Gaos fed of m eements can be obtaned usng the symbos 0,, á, and the eements beng 0,, á, á, á 3 m,... so that fed F* s cosed under mutpcaton wth m eements. The operator
More informationFrequency Granger Causality Test in Cointegration System by Wavelet Analysis
Proceedngs of the 1th WSEAS Internatona Confenrence on APPLIED MATHEMATICS, Daas, Texas, USA, November 1-3, 6 8 Frequency Granger Causaty Test n Contegraton System by Waveet Anayss Yuan Ja-ha, Zhao Chang-hong,
More informationWAVELET-BASED IMAGE COMPRESSION USING SUPPORT VECTOR MACHINE LEARNING AND ENCODING TECHNIQUES
WAVELE-BASED IMAGE COMPRESSION USING SUPPOR VECOR MACHINE LEARNING AND ENCODING ECHNIQUES Rakb Ahmed Gppsand Schoo of Computng and Informaton echnoogy Monash Unversty, Gppsand Campus Austraa. Rakb.Ahmed@nfotech.monash.edu.au
More informationANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)
Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of
More informationAnalysis of Bipartite Graph Codes on the Binary Erasure Channel
Anayss of Bpartte Graph Codes on the Bnary Erasure Channe Arya Mazumdar Department of ECE Unversty of Maryand, Coege Par ema: arya@umdedu Abstract We derve densty evouton equatons for codes on bpartte
More informationREDUCTION OF CORRELATION COMPUTATION IN THE PERMUTATION OF THE FREQUENCY DOMAIN ICA BY SELECTING DOAS ESTIMATED IN SUBARRAYS
15th European Sgna Processng Conference (EUSIPCO 27), Poznan, Poand, September 3-7, 27, copyrght by EURASIP REDUCTION OF CORRELATION COMPUTATION IN THE PERMUTATION OF THE FREQUENCY DOMAIN ICA BY SELECTING
More informationDistributed Moving Horizon State Estimation of Nonlinear Systems. Jing Zhang
Dstrbuted Movng Horzon State Estmaton of Nonnear Systems by Jng Zhang A thess submtted n parta fufment of the requrements for the degree of Master of Scence n Chemca Engneerng Department of Chemca and
More informationGreyworld White Balancing with Low Computation Cost for On- Board Video Capturing
reyword Whte aancng wth Low Computaton Cost for On- oard Vdeo Capturng Peng Wu Yuxn Zoe) Lu Hewett-Packard Laboratores Hewett-Packard Co. Pao Ato CA 94304 USA Abstract Whte baancng s a process commony
More informationA Dissimilarity Measure Based on Singular Value and Its Application in Incremental Discounting
A Dssmarty Measure Based on Snguar Vaue and Its Appcaton n Incrementa Dscountng KE Xaou Department of Automaton, Unversty of Scence and Technoogy of Chna, Hefe, Chna Ema: kxu@ma.ustc.edu.cn MA Lyao Department
More informationResearch Article H Estimates for Discrete-Time Markovian Jump Linear Systems
Mathematca Probems n Engneerng Voume 213 Artce ID 945342 7 pages http://dxdoorg/11155/213/945342 Research Artce H Estmates for Dscrete-Tme Markovan Jump Lnear Systems Marco H Terra 1 Gdson Jesus 2 and
More informationComparison of Regression Lines
STATGRAPHICS Rev. 9/13/2013 Comparson of Regresson Lnes Summary... 1 Data Input... 3 Analyss Summary... 4 Plot of Ftted Model... 6 Condtonal Sums of Squares... 6 Analyss Optons... 7 Forecasts... 8 Confdence
More informationA parametric Linear Programming Model Describing Bandwidth Sharing Policies for ABR Traffic
parametrc Lnear Programmng Mode Descrbng Bandwdth Sharng Poces for BR Traffc I. Moschoos, M. Logothets and G. Kokknaks Wre ommuncatons Laboratory, Dept. of Eectrca & omputer Engneerng, Unversty of Patras,
More informationOptimal Guaranteed Cost Control of Linear Uncertain Systems with Input Constraints
Internatona Journa Optma of Contro, Guaranteed Automaton, Cost Contro and Systems, of Lnear vo Uncertan 3, no Systems 3, pp 397-4, wth Input September Constrants 5 397 Optma Guaranteed Cost Contro of Lnear
More informationEXPERIMENT AND THEORISATION: AN APPLICATION OF THE HYDROSTATIC EQUATION AND ARCHIMEDES THEOREM
EXPERIMENT AND THEORISATION: AN APPLICATION OF THE HYDROSTATIC EQUATION AND ARCHIMEDES THEOREM Santos Lucía, Taaa Máro, Departamento de Físca, Unversdade de Avero, Avero, Portuga 1. Introducton Today s
More information22.51 Quantum Theory of Radiation Interactions
.51 Quantum Theory of Radaton Interactons Fna Exam - Soutons Tuesday December 15, 009 Probem 1 Harmonc oscator 0 ponts Consder an harmonc oscator descrbed by the Hamtonan H = ω(nˆ + ). Cacuate the evouton
More informationMultilayer Perceptrons and Backpropagation. Perceptrons. Recap: Perceptrons. Informatics 1 CG: Lecture 6. Mirella Lapata
Multlayer Perceptrons and Informatcs CG: Lecture 6 Mrella Lapata School of Informatcs Unversty of Ednburgh mlap@nf.ed.ac.uk Readng: Kevn Gurney s Introducton to Neural Networks, Chapters 5 6.5 January,
More informationLower bounds for the Crossing Number of the Cartesian Product of a Vertex-transitive Graph with a Cycle
Lower bounds for the Crossng Number of the Cartesan Product of a Vertex-transtve Graph wth a Cyce Junho Won MIT-PRIMES December 4, 013 Abstract. The mnmum number of crossngs for a drawngs of a gven graph
More informationApproximate merging of a pair of BeÂzier curves
COMPUTER-AIDED DESIGN Computer-Aded Desgn 33 (1) 15±136 www.esever.com/ocate/cad Approxmate mergng of a par of BeÂzer curves Sh-Mn Hu a,b, *, Rou-Feng Tong c, Tao Ju a,b, Ja-Guang Sun a,b a Natona CAD
More informationThe line method combined with spectral chebyshev for space-time fractional diffusion equation
Apped and Computatona Mathematcs 014; 3(6): 330-336 Pubshed onne December 31, 014 (http://www.scencepubshnggroup.com/j/acm) do: 10.1164/j.acm.0140306.17 ISS: 3-5605 (Prnt); ISS: 3-5613 (Onne) The ne method
More informationChapter 6 Hidden Markov Models. Chaochun Wei Spring 2018
896 920 987 2006 Chapter 6 Hdden Markov Modes Chaochun We Sprng 208 Contents Readng materas Introducton to Hdden Markov Mode Markov chans Hdden Markov Modes Parameter estmaton for HMMs 2 Readng Rabner,
More informationCorrespondence. Performance Evaluation for MAP State Estimate Fusion I. INTRODUCTION
Correspondence Performance Evauaton for MAP State Estmate Fuson Ths paper presents a quanttatve performance evauaton method for the maxmum a posteror (MAP) state estmate fuson agorthm. Under dea condtons
More informationNote 2. Ling fong Li. 1 Klein Gordon Equation Probablity interpretation Solutions to Klein-Gordon Equation... 2
Note 2 Lng fong L Contents Ken Gordon Equaton. Probabty nterpretaton......................................2 Soutons to Ken-Gordon Equaton............................... 2 2 Drac Equaton 3 2. Probabty nterpretaton.....................................
More informationTurbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH
Turbulence classfcaton of load data by the frequency and severty of wnd gusts Introducton Oscar Moñux, DEWI GmbH Kevn Blebler, DEWI GmbH Durng the wnd turbne developng process, one of the most mportant
More informationREAL-TIME IMPACT FORCE IDENTIFICATION OF CFRP LAMINATED PLATES USING SOUND WAVES
8 TH INTERNATIONAL CONERENCE ON COMPOSITE MATERIALS REAL-TIME IMPACT ORCE IDENTIICATION O CRP LAMINATED PLATES USING SOUND WAVES S. Atobe *, H. Kobayash, N. Hu 3 and H. ukunaga Department of Aerospace
More informationReactive Power Allocation Using Support Vector Machine
Reactve Power Aocaton Usng Support Vector Machne M.W. Mustafa, S.N. Khad, A. Kharuddn Facuty of Eectrca Engneerng, Unverst Teknoog Maaysa Johor 830, Maaysa and H. Shareef Facuty of Eectrca Engneerng and
More informationA DIMENSION-REDUCTION METHOD FOR STOCHASTIC ANALYSIS SECOND-MOMENT ANALYSIS
A DIMESIO-REDUCTIO METHOD FOR STOCHASTIC AALYSIS SECOD-MOMET AALYSIS S. Rahman Department of Mechanca Engneerng and Center for Computer-Aded Desgn The Unversty of Iowa Iowa Cty, IA 52245 June 2003 OUTLIE
More informationExample: Suppose we want to build a classifier that recognizes WebPages of graduate students.
Exampe: Suppose we want to bud a cassfer that recognzes WebPages of graduate students. How can we fnd tranng data? We can browse the web and coect a sampe of WebPages of graduate students of varous unverstes.
More informationGeneralized Linear Methods
Generalzed Lnear Methods 1 Introducton In the Ensemble Methods the general dea s that usng a combnaton of several weak learner one could make a better learner. More formally, assume that we have a set
More informationCOMBINING SPATIAL COMPONENTS IN SEISMIC DESIGN
Transactons, SMRT- COMBINING SPATIAL COMPONENTS IN SEISMIC DESIGN Mchae O Leary, PhD, PE and Kevn Huberty, PE, SE Nucear Power Technooges Dvson, Sargent & Lundy, Chcago, IL 6060 ABSTRACT Accordng to Reguatory
More informationDynamic Analysis Of An Off-Road Vehicle Frame
Proceedngs of the 8th WSEAS Int. Conf. on NON-LINEAR ANALYSIS, NON-LINEAR SYSTEMS AND CHAOS Dnamc Anass Of An Off-Road Vehce Frame ŞTEFAN TABACU, NICOLAE DORU STĂNESCU, ION TABACU Automotve Department,
More informationDelay tomography for large scale networks
Deay tomography for arge scae networks MENG-FU SHIH ALFRED O. HERO III Communcatons and Sgna Processng Laboratory Eectrca Engneerng and Computer Scence Department Unversty of Mchgan, 30 Bea. Ave., Ann
More informationNONLINEAR SYSTEM IDENTIFICATION WITH SHORTAGE OF INPUT-OUTPUT DATA. S. Feng; J. Chen; X.Y. Tu
NONLINEAR SYSTEM IDENTIFICATION WITH SHORTAGE OF INPUT-OUTPUT DATA S. Feng; J. Chen; X.Y. Tu Department of Automatc Contro, Schoo of Informaton Scence and Technoog, Bejng Insttute of Technoog, 0008, Bejng,
More informationLINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity
LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 31 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 6. Rdge regresson The OLSE s the best lnear unbased
More informationDesign and Optimization of Fuzzy Controller for Inverse Pendulum System Using Genetic Algorithm
Desgn and Optmzaton of Fuzzy Controller for Inverse Pendulum System Usng Genetc Algorthm H. Mehraban A. Ashoor Unversty of Tehran Unversty of Tehran h.mehraban@ece.ut.ac.r a.ashoor@ece.ut.ac.r Abstract:
More informationChapter 11: Simple Linear Regression and Correlation
Chapter 11: Smple Lnear Regresson and Correlaton 11-1 Emprcal Models 11-2 Smple Lnear Regresson 11-3 Propertes of the Least Squares Estmators 11-4 Hypothess Test n Smple Lnear Regresson 11-4.1 Use of t-tests
More informationSolution of Linear System of Equations and Matrix Inversion Gauss Seidel Iteration Method
Soluton of Lnear System of Equatons and Matr Inverson Gauss Sedel Iteraton Method It s another well-known teratve method for solvng a system of lnear equatons of the form a + a22 + + ann = b a2 + a222
More informationAnalysis of CMPP Approach in Modeling Broadband Traffic
Anayss of Approach n Modeng Broadband Traffc R.G. Garroppo, S. Gordano, S. Lucett, and M. Pagano Department of Informaton Engneerng, Unversty of Psa Va Dotsav - 566 Psa - Itay {r.garroppo, s.gordano, s.ucett,
More informationGrover s Algorithm + Quantum Zeno Effect + Vaidman
Grover s Algorthm + Quantum Zeno Effect + Vadman CS 294-2 Bomb 10/12/04 Fall 2004 Lecture 11 Grover s algorthm Recall that Grover s algorthm for searchng over a space of sze wors as follows: consder the
More informationQUARTERLY OF APPLIED MATHEMATICS
QUARTERLY OF APPLIED MATHEMATICS Voume XLI October 983 Number 3 DIAKOPTICS OR TEARING-A MATHEMATICAL APPROACH* By P. W. AITCHISON Unversty of Mantoba Abstract. The method of dakoptcs or tearng was ntroduced
More informationAir Age Equation Parameterized by Ventilation Grouped Time WU Wen-zhong
Appled Mechancs and Materals Submtted: 2014-05-07 ISSN: 1662-7482, Vols. 587-589, pp 449-452 Accepted: 2014-05-10 do:10.4028/www.scentfc.net/amm.587-589.449 Onlne: 2014-07-04 2014 Trans Tech Publcatons,
More informationEEE 241: Linear Systems
EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they
More informationNumerical Investigation of Power Tunability in Two-Section QD Superluminescent Diodes
Numerca Investgaton of Power Tunabty n Two-Secton QD Superumnescent Dodes Matta Rossett Paoo Bardea Ivo Montrosset POLITECNICO DI TORINO DELEN Summary 1. A smpfed mode for QD Super Lumnescent Dodes (SLD)
More informationRESEARCH ARTICLE. Solving Polynomial Systems Using a Fast Adaptive Back Propagation-type Neural Network Algorithm
Juy 8, 6 8:57 Internatona Journa of Computer Mathematcs poynomas Internatona Journa of Computer Mathematcs Vo., No., Month, 9 RESEARCH ARTICLE Sovng Poynoma Systems Usng a Fast Adaptve Back Propagaton-type
More informationSampling-based Approach for Design Optimization in the Presence of Interval Variables
0 th Word Congress on Structura and Mutdscpnary Optmzaton May 9-4, 03, Orando, orda, USA Sampng-based Approach for Desgn Optmzaton n the Presence of nterva Varabes Davd Yoo and kn Lee Unversty of Connectcut,
More informationReliability Sensitivity Algorithm Based on Stratified Importance Sampling Method for Multiple Failure Modes Systems
Chnese Journa o Aeronautcs 3(010) 660-669 Chnese Journa o Aeronautcs www.esever.com/ocate/ca Reabty Senstvty Agorthm Based on Strated Importance Sampng Method or Mutpe aure Modes Systems Zhang eng a, u
More informationHongyi Miao, College of Science, Nanjing Forestry University, Nanjing ,China. (Received 20 June 2013, accepted 11 March 2014) I)ϕ (k)
ISSN 1749-3889 (prnt), 1749-3897 (onlne) Internatonal Journal of Nonlnear Scence Vol.17(2014) No.2,pp.188-192 Modfed Block Jacob-Davdson Method for Solvng Large Sparse Egenproblems Hongy Mao, College of
More informationStructure and Drive Paul A. Jensen Copyright July 20, 2003
Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.
More informationONE DIMENSIONAL TRIANGULAR FIN EXPERIMENT. Technical Advisor: Dr. D.C. Look, Jr. Version: 11/03/00
ONE IMENSIONAL TRIANGULAR FIN EXPERIMENT Techncal Advsor: r..c. Look, Jr. Verson: /3/ 7. GENERAL OJECTIVES a) To understand a one-dmensonal epermental appromaton. b) To understand the art of epermental
More informationis the calculated value of the dependent variable at point i. The best parameters have values that minimize the squares of the errors
Multple Lnear and Polynomal Regresson wth Statstcal Analyss Gven a set of data of measured (or observed) values of a dependent varable: y versus n ndependent varables x 1, x, x n, multple lnear regresson
More informationAssessment of Site Amplification Effect from Input Energy Spectra of Strong Ground Motion
Assessment of Ste Amplfcaton Effect from Input Energy Spectra of Strong Ground Moton M.S. Gong & L.L Xe Key Laboratory of Earthquake Engneerng and Engneerng Vbraton,Insttute of Engneerng Mechancs, CEA,
More informationMultilayer Perceptron (MLP)
Multlayer Perceptron (MLP) Seungjn Cho Department of Computer Scence and Engneerng Pohang Unversty of Scence and Technology 77 Cheongam-ro, Nam-gu, Pohang 37673, Korea seungjn@postech.ac.kr 1 / 20 Outlne
More informationOutput Group Consensus for Heterogeneous Linear Multi-Agent Systems Communicating over Switching Topology
Output Group Consensus for Heterogeneous Lnear Mut-Agent Systems Communcatng over Swtchng Topoogy Jahu Qn Qchao Ma We Xng Zheng Department of Automaton Unversty of Scence and Technoogy of Chna Hefe 37
More information