Study on Project Bidding Risk Evaluation Based on BP Neural Network Theory

Transcription

1 1192 Proceedngs of the 7th Internatonal Conference on Innovaton & Management Study on Proect Bddng Rs Evaluaton Based on BP Neural Networ Theory Wang Xnzheng 1, He Png 2 1 School of Cvl Engneerng, Nanyang Normal Unversty, Nanyang, P.R.Chna, School of fne arts and arts desgn, Nanyang Normal Unversty, Nanyang, P.R.Chna, (E-mal: wxz791023@126.com, hepnglunwen@yahoo.com.cn Abstract By analyzng apprasable factors of engneerng proect bddng rs, a comprehensve evaluaton model of engneerng proect bddng rs based on BP (bac propagaton neural networ s establshed to enhance the valdty of evaluaton n ths paper. By learnng of some effectve samples gven, the extracton and storage of experts nowledge and experence are realzed wth ths model. Two smulaton examples show that the proposed comprehensve evaluaton model of engneerng proect bddng rs based on BP neural networ s reasonable and feasble. The purpose of ths paper s to provde a scentfc bass for the bddng decson-mang. Key words Engneerng proect; Neural networ; Rs evaluaton 1 Introducton The purpose of engneerng proect s bddng s to lower cost and to mprove proect qualty by wnnng the bd n competton. One of the mportant lns s bddng rs apprasal, whch s also tactcs and technologes competton between bddng enterprses. Several researches on rs apprasal regardng engneerng proects have been conducted at present, and some practcal apprasal methods have been proposed at present. For example, the entropy method[1], proecton pursut(pp method[2], Analytc Herarchy Process(AHP method[3], fuzzy comprehensve evaluaton[4], etc. All these apprasal methods have ther lmtatons. For nstance, In the PP method realzaton process, proecton drecton, proecton value and proecton ndcators functon, etc., need to be changed accordng to actual problems, and t s worth a further studyng. The AHP method requres apprasal ndcators to be ndependent, etc., but many factors n mult-level system are nterrelated and nfluence each other, and ther ndcator systems are not ndependent structure. Therefore, ths paper proposes an apprasal method of engneerng proect bddng rs based on artfcal neural networ technology, and establshes apprasal model of proect bddng rs based on BP neural networ. Its goal s to apprase proect bddng rs effectvely and to provde a scentfc bass for the bddng decson-mang. 2 Indcators System of Engneerng Proect Bddng Rs Apprasal 2.1 Constructon of apprasal ndcators In order to analyze the proect bddng rs comprehensvely, frstly we should determne the proect bddng rs nfluence factors. Accordng to the roots where rs factors produce and some correlated domestc/foregn lterature materals, based on some ndcator apprasable prncples of commensurablty, representaton, measurablty and foresght, the rs factors n engneerng proect are gven n Table 1. Apprasal ndcators used to decde effectual factor are natural rs, poltcal rs, economc rs, competton rs, scene rs, techncal rs, equpment materal rs, owner rs, contractor rs, overseeng mechansm, management mechansm and decson-mang level. Accordng to the apprasal rule, the ey effect factors are obtaned. A framewor wth 12 ndcators s lsted n Table 1, n whch there are 4 ndcators for External Envronment, 3 ndcators for Proect Manbody, 4 ndcators for Proect Owner and 1 ndcators for Management Decson-Mang. 2.2 Normalzaton of apprasal ndcators In order to elmnate the dfference of magntude and unts, the orgnal data should be normalzed before Artfcal Neural Networ processng. Because the dmensons and quanttes of dfferent ndcators dffer greatly, they should be transformed to a certan range and n a non-dmensonal form. Thus the comparsons would be meanngful. In ths research, a normalzaton transformaton method was utlzed to transform all the parameters and varants to non-dmensonal quanttes. All ndcators n proect bddng rs are grouped nto two nds: postve and negatve ones. If an ndcator ncreases whle bd rs apprasal result ncreases, t s grouped as postve ones. Vce versa, negatve ndcators refer to the opposte characterstcs. The normalzaton processes of apprasal ndcators are lsted as follows:

2 Proceedngs of the 7th Internatonal Conference on Innovaton & Management 1193 (1 Postve ndcators: Table 1 x u = u 1( u u u mn ( u mn u mn 0( u u < u mn < u Indcators and Influence Factors of Proect Bddng Rs Apprasal Rs categores External Envronment Rs Proect Owner Rs Proect Manbody rs Management Decson-ma Specfc ndexes natural polcy economy Status of competton owner contractor Overseeng qualty Management mechansm Technology Equpment materal Tenderer s management Decson-mang level Indcators descrbe Clmate condton; Natural dsaster Polcty envronment; Professon polcy Socety economy ; Professon economy; Contractor economy Economy Status; Intellgence, prestge, achevement; Authorzed tendency Intellgence, prestge, achevement; Constructon techncal technology; Management level Geology poston and scene New craft; Technology complex Specal equpment; Equpment power; Materal supply (2 Negatve ndcators: 1( u u u u x = ( u mn < u < u u u mn 0( u u mn Where urepresents the orgnal data of any rs ndcator, ( = 1, 2, L,12 u mn and u are respectvely the mnmum and mum data of ndcator n the standard framewor n Table 1. By ths way, we transform the orgnal data nto standardzed values n the range of [0,1], and they are non-dmensonal values. 3 Artfcal Neural Networs Model of Proect Bddng Rs Apprasal 3.1 Prncple of the model of bddng rs evaluaton Based on ANN The prncple of the model of engneerng proect bddng rs evaluaton based on ANN(Artfcal Neural Networ s: Through experts successful experences and data, we establsh a mult-nputs and mult-outputs ANN comprehensve apprasal model. Frstly, ANN model taes the bddng rs ndcators normalzaton values as nput value and the output s the apprasal value of the proect. Secondly, ANN model wll tran the networ through enough learnng data and mae dfferent nput vector obtan correspondng output vector and format experts experence, nowledge as well as ndcator tendentous understandng of proect bddng rs evaluaton. As ts smulaton s very accurate, the traned ANN model can be used as one nd of effectve tool to evaluate unnown proect bddng rs, t can output the proect bddng rs apprasal degree and realze a comprehensve evaluaton of engneerng proect bddng rs. 3.2 BP networ model structure

3 1194 Proceedngs of the 7th Internatonal Conference on Innovaton & Management Expectancy Output vector Input layer Hdden layer Output layer Fgure 1 BP Networ Model Structure Accordng to the comprehensve apprasal characterstcs of proect bddng rs, a 3-layers-structure bac propagaton (BP neural networ s proposed for mult-layer artfcal neural networ wth sgmod functon. It s composed of nput layers, hdden layers and output layers. In ths research, nput layers refer to proect bddng rs ndcators and output layers refer to apprasal result. Based on some correlated cases and lteratures, nput layer s composed of twelve nput neural unts, namely natural condton, poltcal condton, current economc condton, competton rs, geology poston, technology factor, equpment & materal, owner factor, contractor factor, overseeng mechansm, management mechansm and decson-mang level; output layer s composed of fve output neural unts, namely rs evaluaton degree very hgh, hgher, medum, lower and very low; hdden layer s determned by probe and experence, here we select 25 hdden neural unts. The BP net model structure s pctured n Fgure BP networ learnng method BP neural networ learnng method s dvded nto two stages: In the frst stage, BP networ calculates each neural unts nput and output values from nput layers to output layers, whch belongs to fore transmsson; n the second stage, BP networ calculates each neural unts output errors from output layers to nput layers and then adusts each layer s wegh-value as well as neural unts threshold value by error gradent drop prncple, whch belongs to bac transmsson. We suppose nput sample T T space s X = ( x1, x2, L, x, output sample space s Y = ( y1, y2, L, y, where T denotes numbers of the sample, wegh-value between nput layer and output layer s w, devaton value s b, wegh-value between hdden layer and output layer s w, devaton-value s b. Then the BP networ learnng steps are as follows: Step 1: Intalze wegh-value w w and devaton-value b b, n order to establsh small random numbers to avod networ saturate and unusual stuaton; Step 2: Input learnng data and calculate networ output values, we have Hdden layer: o = f ( x = f ( w x b p Output layer: o p = f ( o p = f (( w x b b x Where f (x can be taen as a sgmod functon, namely f ( x = 1/(1 + e 1 2 Step 3: Calculate each layers error sgnal, namely E = ( T p o p, where T p s a 2 expect error, Here output layer error sgnal s e = T o then nput layer error sgnal s ' 2 e = f ( o e w = (1 b w p e w ; Step 4: Adust wegh-value and devaton-value ; w = w + Δw, = w + Δw, b = b + Δb, b = b + Δb, Δw = η e x, Δw = η e o p, p p

4 Proceedngs of the 7th Internatonal Conference on Innovaton & Management 1195 Δb = η e, Δb = η e Where η refers to learnng rate. Step 5: Repeat the learnng process (step 2 to 4 for n tmes to get a normal dstrbuton and the average value. 4 Comprehensve Apprasal Model Tranng and Testng Table 2 ANN Net-Tranng Input Value No X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X Table 3 Factual Output and Expectaton Output of ANN No Expectaton Output Factual Output To tran BP neural networ well, frstly we should determne learnng data s nput/output value. Learnng data nput-value can be obtaned through a normalzng way. In terms of the actual stuaton of proect bddng rs apprasal, output-value could be grouped nto 5 levels, namely very hgh, hgher, medum, lower and very low, n whch vector (1,0,0,0,0 ndcates rs very hgh, vector (0,1,0,0,0 ndcates hgher, vector (0,0,1,0,0 ndcates medum, vector (0,0,0,1,0 ndcates lower and vector (0,0,0,0,1 ndcates very low. As analyzed above, we mport the frst 10 groups of data as tranng data n table 1 and the last one as testng data to model emulate by Matlab software, and run the emulate model to begn the tran, we select overall error s 0.01, hdden layer s learnng rate 0.8 and output layer s learnng rate 0.6. Tranng nput-values of ANN model are lsted n table 2. By tranng 1100 tmes, ANN net-tranng error whch precson s , but the goal precson s , After net-tranng 2000 tmes, ANN net-tranng tends to be stable, whch teraton s BP networ actual output-value comparson to expectaton output-value s lsted n Table 3. By comparng the tranng result n Table 3, BP net factual output-value s consstent wth expectaton output-value completely. In order to test BP net structure bult above, now we mport last group of data (In Table 2 to BP net model to examne ts feasblty n terms of ther output results. By calculatng and tranng, the BP networ factual output-value vector s (0.0037,0.9468, , , , whch s consstent wth expectaton output result(0,1,0,0,0. Therefore, proect bddng rs evaluaton method based on ANN can obtan satsfacton results. 5 Practcal Applcatons As dscussed above, we utlze ths ANN model to evaluate proect bddng rs of Jngyng hghway n contract secton A2. Accordng to ths proect s correlated statstc data and some nvestgatons of external envronments rs, proect man body rs, proect owner rs as well as management decson-mang rs, by normalzng these evaluaton ndcators, a quantfcaton value vector wll be acheved, namely (0.3758, , , , , , ,0.0385, ,

5 1196 Proceedngs of the 7th Internatonal Conference on Innovaton & Management , , Inputtng these vector data to ANN model establshed above, by calculatng and tranng n ANN model, we get a output-value vector, namely (0.0014, , , , Through ANN net output results, we now ths proect bddng rs degree s lower. 6 Conclusons Ths paper constructs an ndcator system of engneerng proect bddng rs apprasal and establshes a rs apprasal model based on ANN. Through real dagnoss research, the ANN model s capable of apprasng proect bddng rs comprehensvely. When we apprase unnown proects bddng rs by ANN model, the ANN model can realze a drect and obectve apprasal result through reappearng experts nowledge and experence. Its goal s to provde a scentfc bass for the bddng decson-mang. References [1] Yue Zhqang, Zhang Qang. Study on the applcaton of decson approach based on weght of Entropy n publc Bddng of proect management [J]. Chnese Journal of Management Scence, 2004(10: 83-86(In Chnese [2] Lu Rao, We Xaopng. Proecton Pursut model based on BOT model of Rs Evaluaton[J]. Statstcs and Decson, 2006(8: 9-10(In Chnese [3] Ln Feteng, Hou Duzhou. Fuzzy analyss on the AHP of proect rs n nternatonal proect contractng[j]. Journal of X an Unv of Arch&Tech,2003 (3: 63-66(In Chnese [4] Ja Hongun, Wu Shourong. Desgn and Study on Apprasal bd Models of Engneerng Proect[J]. System Engneerng Theory & Practce, 1999(2 (In Chnese [5] Hll T Marquezl, Connor M O, et a1.artfcal Neural Networ Model for Forecastng and Decson Mang[J].Internatonal Journal of Forecastng,1994, (10: (In Chnese [6] Yan Pngfan, Zhang Changshu. Artfcal Neural Networ and Smulaton Calculaton [M].Beng:Tsnghua Unversty Press, 2000(In Chnese