INTEGRATED MODEL FOR PRODUCT QUALITY FORECASTING SYSTEM USING GREY THEORY AND NEURAL NETWORK

Transcription

1 Journal of Theorecal and Appled Informaon Technology 5 h November 202. Vol. 45 No JATIT & LLS. All rghs reserved. ISSN: E-ISSN: INTEGRATED MODEL FOR PRODUCT QUALITY FORECASTING SYSTEM USING GREY THEORY AND NEURAL NETWORK JIHONG PANG, WEI XUE, HONGMING ZHOU, FENGPING LI College of Mechancal and Elecronc Engneerng, Wenzhou Unversy, Wenzhou , Zhejang, Chna ABSTRACT Hgh-qualy producs are consdered as one of he mos mporan pracces for achevng success. However, s really hard o predc and forecas he produc qualy due o some undeermned parameers. In order o forecas produc qualy from varous aspecs, we propose an negraed model of ulzng grey heory and neural nework. In hs paper, he grey forecasng model for produc qualy s esablshed by applyng grey heory. Grey model s appled o compue an aggregaed effcency score based on he npu and oupu daa. Snce quanave facors are dffcul o mahemacally manpulae when forecasng he effcency n neural nework, a forecasng model s developed for produc qualy based on grey neural nework model. In addon, analycal capably of he proposed mehod can reduce he number of ranng samples. In our case, hs approach s demonsraed on a real and complee daase of 36 samples for produc qualy. Fnally, an example s gven o explan he use and effecveness of he proposed compuaonal approach. As a resul of hs research, grey neural nework can now be adequaely appled o forecas he produc qualy. Keywords: Produc Qualy, Forecasng Sysem, Grey Theory, Neural Nework INTRODUCTION Produc qualy s srongly affeced by he qualy of he exernal envronmen. In recen years, he concep of produc qualy has been ransformed from fness for use o cusomer sasfacon[]. Today, produc qualy s facng new challenges as well as n he years ahead n he weny-frs cenury[2, 3]. Achevng success n a more and more careful marke, he produc qualy mus have grea progress[4]. However, he developmen and manenance of forecasng sysem of produc qualy has proven o be an enormous and complex process[5]. An effecve forecasng sysem of produc qualy s of paramoun mporance for many manufacures. On he oher hand, manufacure enerprses mus work well and brng an awareness of he need for hgh-qualy producs for her cusomers[6]. I s mporan for fuure decson-makng where decson-makers are neresed n knowng he changes requred n combnng npu resources so can be classfed no a desred produc qualy[7]. The forecasng of dfferen levels of produc qualy could help he managemen n denfyng he qualy of desgn and manufacure process o work ou approprae nervenons o mprove he qualy. Ths sudy has denfed he forecasng problems of falng o deal wh dversfcaon and develops an erave approxmaon procedure o deal wh. Ths paper allows us o help qualy professonals and qualy organzaons n makng beer forecas n he fuure. The am of hs paper s o consder he recen developmens n forecasng sysem for produc qualy. Grey heory s nowadays consdered o be one of he mos effecve ools ha can be adoped for makng a sraegc forecasng[8, 9]. Moreover, neural nework as a forecasng mehod has been very commonly used n varous felds[0]. Snce he grey neural nework s mosly used o opmze ndeermnae sysem, hs paper akes advanage of grey heory and negraes neural nework o buld he opmal forecasng sysem for he produc qualy[]. Ths arcle s organzed as follows. The paper wll frs dscuss some ssues on he forecasng sysem of produc qualy and her dffcules n defnon and predcon. I wll hen look a he use and applcaon of case sudes for forecasng sysem for produc qualy. Then, he followng secon descrbes he negraed model for produc qualy forecasng sysem. In he fnal secon, we offer a dscusson of he resuls and he conclusons drawn from he research. 285

2 Journal of Theorecal and Appled Informaon Technology 5 h November 202. Vol. 45 No JATIT & LLS. All rghs reserved. ISSN: E-ISSN: INTEGRATED MODEL FOR PRODUCT QUALITY FORECASTING SYSTEM In recen years, many researchers have proposed varous forecasng mehods for dealng wh he mprecse and ambguous daa of produc qualy. In he area of produc qualy forecasng, here have been receved sgnfcan aenon over he las decade. And here was a need o be able o solve he forecasng problem of produc qualy whou affecng he produc srucure and performance. In he las weny years, many research sudes of produc qualy forecasng have focused on he new mehodologes for mprovng he forecas accuracy. To enhance he relably and valdy of produc qualy forecasng, an negraed model based on grey heory and neural nework was bul n hs paper[2]. Frs, he grey heory can avod complex calculaon n all mulplers on qualy ndcaors so ha can handle an occurrence of mulple forecasng. Secondly, he grey neural nework model was appled o forecas all produc qualy by usng a lmed number of produc qualy ndcaors n dfferen reference ses. So, grey neural nework s a useful ool for forecasng he produc qualy. Furher, neural nework was appled o enumerae he role of seleced qualy facors and procedure degradaon wh he fnal am of opmzng he calculae process. I was decded ha, n order o gan some quanave knowledge of he forecasng, a sysem should be bul by he negraed model based on grey heory and neural nework. On a general level, he use of forecasng sysem o forecas produc qualy was mos successful and he sysem was mplemened wh mnmum calculae procedure. Through grey heory and neural nework, here can be large mprovemens n hgher produc qualy o mee nernaonal sandards. The man objecve of hs paper s o explore polcymakers audes and percepons abou forecasng process. Ths wll help manufacurng companes mprove he skll of produc qualy predcon. The negraed model for produc qualy forecasng sysem based on grey neural nework s shown n Fgure. Then, we can dvde he several seps of he algorhm no separae modules for compose analyss. Esablshng forecasng model of produc qualy Deermnng forecasng sysem Developng and buldng he grey heory model for forecasng process Arrangng he daa and devsng he daabase Choosng he bes need funcon for produc qualy forecasng Yes The end of forecasng process Buldng opmal model and program Esablshng negraed model of combnaon forecasng Are forecasng resuls reasonable? Grey heory Neural nework Geng he fnal produc qualy forecasng resuls No Fgure : The Inegraed Model For Produc Qualy Forecasng The followng seps show how o apply he negraed model o forecas he produc qualy based on grey heory and neural nework. I wll be a process ha unfolds over wo sages. In he frs sage, he dscree grey opmzaon model needs o be consruced. The grey predcon s he key par of grey heory. Grey heory s a powerful ool for solvng he problems of uncerany wh few daa[3]. Grey heory has obaned he fas developmen based on modern sysems scence and uncerany sysems heory snce he early 980s[4, 5]. The cardnal prncple of grey heory s ha relaonshps beween hose elemens can be defned by usng he close geomercal paerns of sequence curves[6]. From comng no beng, grey heory has been wdely appled n varous felds[7, 8]. Frs of all, he normalzaon of cos-ype ndces s done usng he followng equaon: * max x () l x () l x () l = max x ( l) mn x ( l) () Accordngly, he normalzaon of benef-ype ndces s done n he followng: 286

3 Journal of Theorecal and Appled Informaon Technology 5 h November 202. Vol. 45 No JATIT & LLS. All rghs reserved. ISSN: E-ISSN: * x () l mn x () l x () l = max x ( l) mn x ( l) (2) In he nex sep, he GM (,) grey model s appled o fng and forecasng he produc qualy by usng grey heory. Snce a lo of our analyss needs o be aken uncerany facors no accoun, he prme objecve s o buld me seres vews no he daa. For hs reason, we mus assume abou he use of hese me seres, as he followng formula defnon shows. X = ( X (), X (2),, X ( m) ) (3) (0) (0) (0) (0) () () () () In he nex sep, he accumulaed sequence can be esablshed by he followng formula: X = ( X (), X (2),, X ( m) ) (4) () () () () () () () () Y = ( Y (), Y (2),, Y ( m) ) (5) Y () () () () () () () () () () () () Y( ) () l + Y( ) () l () l = (6) 2 Where, = 2, 3,, m. On he nex lne, we are creang an expresson for GM (,) grey model n he followng: X () l + ey () l = f (7) (0) (0) () () By usng he buldng mechansm of grey forecas model, we can fnsh many accumulaed sequence n he followng formulae: () l (0) X() ( l) = X() ( ), ( =, 2,, m) (8) = Then, he dfference equaon of GM (,) model can be confrmed by he followng expresson defnon. d () X ( ) () ex () d + = u (9) So he lnear equaon se can be solved by means of leas square mehod by usng he followng formula: e β = = u T ( ) A T A AY (0) Besdes, he arrangemen process s compleed by machng he followng marx A and marx Y: () () ( X() (2) + X() (2)) / 2 A = () () ( X() ( m) + X() ( m )) / 2 () (0) X ()(2) Y = (2) (0) X() ( m) Therefore, he me respond funcon of grey predcon model can be calculaed by he followng formula: X () l = X () l X ( l ) (3) (0) () () () () () In he second sage, he predcon model of neural nework s suded. Neural nework s a branch of arfcal nellgence. So he neural nework s an effcen mehod for learnng complex mappngs from a se of examples[9-2]. Based on he bologcal nervous sysem, he srucures of neural nework can be well esablshed[22, 23]. A revew of he developmens n neural nework has been well dscussed n many hesses[24-26]. Based on grey heory, hs paper also has used he neural nework model o forecas he produc qualy. The common neural nework modelng s shown n Fgure 2. Here, we can ge he npu daa for neural nework modelng from he grey predcon model. Inpu layer Connoaed layer Oupu layer IP IP 2 IP n Fgure 2: The Neural Nework Modelng OP OP 2 OP n Then, he weghng marx of oupu layer can be compued by usng hs formula: e = m j= ( y x ) j j 2 (4) Where, =, 2,, m. 287

4 Journal of Theorecal and Appled Informaon Technology 5 h November 202. Vol. 45 No JATIT & LLS. All rghs reserved. ISSN: E-ISSN: And hs wll provde some references for us o choose he rgh weghng marx of oupu layer. Nex, he number of nodes of each hdden layer of he nework can be deermned: C = ( IP + OP ) 2 + ε (5) Where, IP s he number of nodes of npu layer, OP s he number of nodes of oupu layer, and ε 0. Fnally, he error merc of sample collecon for neural nework by usng he followng formula: m e= e (6) = From wha we have menoned above, he new mehod s a mxed predcon mehod ha s composed of grey heory and neural nework. 3 CASE STUDY The man purpose of hs paper s o provde forecasng nformaon for produc qualy based on grey heory and neural nework. In hs sudy, he forecasng sysem was developed n close consulaon wh produc managemen deparmen and enerprse managers. Ths approved approach s very applcable when dealng wh complex and srong nonlnear forecasng processes. In hs paper, sx qualy ndcaors (QIs) of produc qualy were seleced for close observaon n he forecasng process. Aenon was focused on he daa processng as was exremely mporan. And he usefulness and effecveness of he developed soluon concep depends on basal daa. I was found ha, alhough records were kep of he forecasng process, here was no analyss of he orgnal daa whou specal mehod. Based on grey heory neural nework, he forecas sysem of produc qualy could be carred ou. In general, a complee produc qualy forecas sysem should nclude he followng sx QIs: funconaly (QI), credbly (QI2), echncal performance (QI3), servceably (QI4), economy (QI5) and envronmenal effec (QI6). Then, he normalzed daa of sx QIs are shown n Table I. In hs sage, we can fnd an adjused effcency score for each QI. Here, he 36 samples were spl no several ses for ranng and esng by usng grey heory and neural nework. Frsly, he forecasng of he daa of each QI s verfed hrough grey heory modelng. In he nex, he proposed mehod consss on a neural nework model of ran. TABLE I : Normalzed Daa Of Sx Qualy Indcaors QI QI 2 QI 3 QI 4 QI 5 QI The sascal daa from Table I was used o calculae he me seres for sx QIs. Then, he accumulaed sequence can be esablshed by usng grey heory. The GM(,) grey model and grey forecasng algorhm can be used o predc he endency of QIs. Once hese daa were analyzed based on grey heory, we can dscover he hdden relaonshp of hese QIs for he produc qualy. 288

5 Journal of Theorecal and Appled Informaon Technology 5 h November 202. Vol. 45 No JATIT & LLS. All rghs reserved. ISSN: E-ISSN: Then, we can draw he characersc curve of sx QIs n he followng fgures Fgure 3: Characersc curve of funconaly (QI ) Fgure 7: Characersc curve of economy (QI 5 ) Fgure 4: Characersc curve of credbly (QI 2 ) Fgure 8: Characersc curve of envronmenal effec (QI 6 ) In he nex sep, we can ge he ranng errors dsrbuon by he ranng mes based on grey neural nework. Fgure 9 shows he ranng errors for produc qualy forecasng sysem ranng errors Fgure 5: Characersc curve of echncal performance (QI 3 ) errors Fgure 6: Characersc curve of servceably (QI 4) 进化次数 ranng mes Fgure 9: The ranng errors based on grey neural nework Fnally, we can selec he opmal mahemacal model based on he errors, and hen gves he model of ran. The dfference of he real and forecased resul from 3 samples o 36 samples s shown n Fgure 0. The blue curve shows he resul of he suaon. And he red curve shows he resul n he forecasng process. 289

6 Journal of Theorecal and Appled Informaon Technology 5 h November 202. Vol. 45 No JATIT & LLS. All rghs reserved. ISSN: E-ISSN: Fgure 0: The Comparson Beween The Forecasng Daa And The Acual Daa Based on he above, hs case has presened he comparson of compued and real resuls, and he forecasng error s whn 7% hrough expermenal verfcaon. The resuls of he case sudy show ha he predcon values gven by heory and neural nework are beer han ha of oher mehods. And he adjusmen of forecasng process when beer exernal nformaon becomes avalable s an area requrng more aenon. 4 CONCLUSIONS Ths paper has presened an negraed model of produc qualy forecasng sysem based on grey heory and neural nework. To he bes of our knowledge, hs paper conrbues o he exsng leraure by developng an nnovave research framework. Besdes, he produc qualy can be forecased by usng he new applcaon of grey neural nework model wh sparse npu and oupu daa. And hs wll help busness people and polcymakers o fnd an effcen way o acheve beer produc qualy for her cusomers. The proposed approach s useful n dealng wh such a lmed number of produc samples, and he resuls are more represenave and persuasve. Ths sudy concludes ha such analyss can be que useful n a varey of crcumsances. To documen her praccal mplcaons, hs sudy has appled he proposed approach o forecas he qualy of producs. As a consequence, hs sudy fnds ha grey neural nework s a forecasng ool for he produc qualy. So, grey neural nework s useful for he busy leaders and enerprse managers. Furhermore, we have presened some useful mplcaons and applcaons boh for academcans and praconers n hs paper. The drecons of he approved mehod wll be dscussed and offered n fuure research. ACKNOWLEDGMENTS The work of hs paper s suppored by he Naural Scence Fund of Guangdong Provnce (No. S ), he Scence and Technology Plannng Projec of Zhaoqng Cy (No. 200F006, No. 20F00), and he Research Inaon Fund of Zhaoqng Unversy (No. 202KQ0). REFRENCES: [] K. Das, A. H. Chowdhury, "Desgnng a reverse logscs nework for opmal collecon, recovery and qualy-based produc-mx plannng", Inernaonal Journal of Producon Economcs, vol. 35, No., 202, pp [2] A. Rahm, M. Shakl, "A abu search algorhm for deermnng he economc desgn parameers of an negraed producon plannng, qualy conrol and prevenve manenance polcy", Inernaonal Journal of Indusral and Sysems Engneerng, vol. 7, No. 4, 20, pp [3] V. Fann, P. Buol, R. Pergreff, P. Mason, "Lfe cycle assessmen of Ialan hgh qualy mlk producon. A comparson wh an EPD sudy", Journal of Cleaner Producon, vol. 28, 202, pp [4] D. P. McInyre, "Where here's a way, s here a wll? Insalled base and produc qualy n a nework ndusry", Journal of Hgh Technology Managemen Research, vol. 22, No., 20, pp [5] D. Dey, S. Kumar, "Reassessng daa qualy for nformaon producs", Managemen Scence, vol. 56, No. 2, 200, pp [6] I. Kyrakds, K. Karazas, G. Papadouraks, J. A. Ware, "Usng arfcal nellgence mehods o undersand and forecas amospherc qualy parameers", Engneerng Inellgen Sysems, vol. 20, No. -2, 202, pp [7] S. Palan, S. Y. Long, P. Tkalch, "An ANN applcaon for waer qualy forecasng", Marne Polluon Bullen, vol. 56, No. 9, 2008, pp [8] D. F. Mlle, G. R. Weckman, W. A. Young, J. E. Ivey, H. J. Carrck, G. L. Fahnensel, "Modelng mcroalgal abundance wh arfcal neural neworks: Demonsraon of a heursc 'Grey-Box' o deconvolve and quanfy envronmenal nfluences", Envronmenal Modellng and Sofware, vol. 38, 202, pp

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