田口方法於多元特性因子預測模式及倒傳遞網路拓璞參數之研究_

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題名：	田口方法於多元特性因子預測模式及倒傳遞網路拓璞參數之研究
作者：	黃建裕
作者(外文)：	Chien-Yu Huang
校院名稱：	國立成功大學
系所名稱：	工業與資訊管理學系碩博士班
指導教授：	王泰裕
學位類別：	博士
出版日期：	2006
主題關鍵詞：	倒傳遞網路；遺傳演算法；田口方法；Back-propagation network；Genetic algorithm；Taguchi method
原始連結：	連回原系統網址
相關次數：	被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0) 排除自我引用:0 共同引用:0 點閱:1

　　由於週遭環境的快速變遷，產品生命週期(product life cycle)正以極快的速度縮短中，因此身為一個管理的決策者(decision maker)，如何準確有效的預測到產品的銷售量，將是一個極富挑戰的課題。本研究提出一個具有多元重要特性因子的預測(forecasting)模式，經由田口方法(Taguchi method)的內直交表(Inner array)，架構成一個部分因子實驗設計(fractional factorial design)，以研究各因子的主效力(main effects)及其間相互的作用力(interaction)。另外，一個外直交表(outer array)則討論模式所採納的預測方法，針對其本身所可能產生的干擾因素(噪音)，依循田口方法創造的重複實驗計畫的準則以降低噪聲因素(noise factor)的干擾。期能以較少的實驗資料來獲得比傳統方法更多且穩健(robust)的資訊。至於預測方法，倒傳遞網路(back-propagation network, BPN)是現今類神經網路(artificial neural network, ANN)中最被廣泛應用於解決實務問題的一種預測模式。但是，倒傳遞網路在求解過程中却有容易落於區域最佳解(local optimum)陷阱的問題，即所求得的解並非是具有全域最佳解(global optimum)的最佳方案。這個現象，也使得倒傳遞網路的預測能力，經常表現得不穩定而無法獲得一致性的結果。因此，啟發式演算法(heuristics)就被寄望於能夠克服上述的問題，其中，遺傳演算法(genetic algorithm, GA)便是最具有潛力的演算法之一。但是，倒傳遞網路和遺傳演算法本身均含有許多未確定的拓璞結構參數或其他重要參數，且其參數的因子水準(levels)亦未被正確而有效率的引用。然而，透過一個適當的網路拓璞結構，來建立一個有效率的可行解搜尋區域(feasible solution space)，對於增進管理者的決策能力和決策質量，却是極為重要的。因此，本研究將進一步建構一個內直交表用來配置控制因子，即倒傳遞網路的拓樸參數及其網路屬性參數。同時，另一個外直交表則被安排來配置代表噪聲因子的遺傳演算法參數。此外，本研究亦將集中於季節變動和混沌時間序列(chaotic time series)的預測問題上。在研究對象的選取上，我們選擇臺灣電力公司(Taiwan Power Company, R.O.C.)的售電量和混沌時間序列(Mackey and Glass, 1997)為分析資料，用於實證本論文所提出的模式。結果顯示，依據本研究所提出的預測方法來進行分析，可得到較佳的績效和較可靠的穩健性。

以文找文

　To satisfy the volatile nature of today's markets, businesses require a significant reduction in product development lead times. Consequently, the ability to develop product sales forecasts accurately is of fundamental importance to decision-makers. Over the years, many forecasting techniques of varying capabilities have been introduced. The precise extent of their influences, and the interactions between them, has never been fully clarified, though various forecasting factors have been explored in previous studies. Accordingly, this study adopts the Taguchi method to calibrate the controllable factors of a forecasting model. An inner orthogonal array was constructed for the time series related controllable factors. An experimental design was then performed to establish the appropriate levels for each factor. At the same time, an outer orthogonal array was used to incorporate the inherited parameters of the forecasting method as the noise factors of the Taguchi method simultaneously. As to the forecasting method, a heuristic technique such as the genetic algorithm (GA) has been recognized as a potential method to establish the parameter and topology settings, which optimize the back-propagation network (BPN) performance. However, there are too many undetermined parameters of the BPN and the genetic algorithm themselves, and the impact and interactions of these controllable factors have not been fully explored as they interact simultaneously. Hence, it is desirable to develop a more methodical approach to identifying these parameters’ values. The solutions obtained using the proposed forecasting model, which are combined with the Taguchi method, are compared with the results presented in previous studies. Illustrated examples, employing data from a power company and chaotic time series, serve to demonstrate the thesis. The results show that the proposed model permits the construction of a better forecasting model through the suggested data collection method.

以文找文

Abhijit, D., Hojjat, A., 2003. Neural network model for rapid forecasting of freeway link travel time, Engineering Applications of Artificial Intelligence 16 607-613.
Box, G.P., Jenkins, G.M., 1976. Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco.
Casab, J., Orsolya, D., Anna, L., Eya, A., Lstyan, N., 2003. Taguchi optimization of ELISA procedures, Journal of Immunological Methods 223 (2) 37-146.
Chen, L.H., 1997. Designing robust products with multiple quality characteristics, Computers & Operations Research 24 (10) 937-944.
Cui, W.Z., Zhu, C.C., Bao, W.X., Liu, J.H., 2004. Prediction of the chaotic time series using support vector machines, Acta Physica Sinica 53(10) 3303-3310.
Delurgio, S.A., 1998. Forecasting Principles and Applications, McGraw-Hill, Boston.
Goffe, W.L., Ferrier, G.D., Rogers, J., 1994. Global optimization of statistical functions with simulated annealing, Journal of Econometrics 60 65-99.
Goldberg, D.E., 1989. Genetic Algorithms in Search, Optimization, and Machine Learning, Addison Wesley, New York.
Gracia, J.D., Saravia, M.L.M.F.S., Araujo, A.N., Lima, J.L.F.C., Valle, M.D., Poch, M., 1997. Evaluation of natural computation techniques in the modeling and optimization of a sequential injection flow system for colorimetric iron (Ⅲ) determination, Analytica Chimica Acta 348 143-150.
Heckerling, P.S., Gerber, B.S., Tape, T.G., Wigton, R.S., 2004. Use of genetic algorithm for neural networks to predict community-acquired pneumonia, Artificial Intelligence in Medicine 30 71-84.
Jiang, N., Zhao, Z., Ren, L., 2003. Design of structural modular neural networks with genetic algorithm, Advances in Engineering Software 34 17-24.
Kalaitzakis, K., Stavrakakis, G.S., Anagnostakis, E.M., 2002. Short-term load forecasting based on artificial neural networks parallel implementation, Electric Power Systems Research 63 185-196.
Kamrani, A.K., Gonzalez, R., 2003. A genetic algorithm-based solution methodology for modular design, Journal of Intelligent Manufacturing 14 599-616.
Khaw, J.F.C., Lim, B.S., Lim, L., 1995. Optimal design of neural networks using the Taguchi method, Neurocomputing 7(3) 225-245.
Khoei, A.R., Masters, I., Gethin, D.T., 2002. Design optimization of aluminum recycling processes using Taguchi technique, Journal of Materials Processing Technology 127 (1) 96-106.
Khoo, L.P., Loi, M.Y., 2002. A tabu-enhanced genetic algorithm approach to agile manufacturing, International Journal of Advanced Manufacturing Technology 20(9) 692-700.
Ko, D.C., Kim, D.H., Kim, B.M., 1999. Application of artificial neural network and Taguchi method to perform design in metal forming considering workability, International Journal of Machine Tools & Manufacture 39 771-785.
Kumar, K., Jain, V.K., 1999. Autoregressive integrated moving averages (ARIMA) modeling of a traffic noise time series, Applied Acoustics 58 283-294.
Kyong, J.O., Ingoo, H., 2000. Using change-point detection to support artificial neural networks for interest rates forecasting, Expert Systems with Applications 19 105-115.
Lehmann, E.L., 1975. Nonparametrics: Statistical Methods Based on Ranks, Holden-Day, San Francisco.
Lim, C., McAleer, M., 1999. A seasonal analysis of Malaysian tourist arrivals to Australia, Mathematics and Computers in Simulation 48 573-583.
Lin, T.J., Juang, R.C., Chen, Y.C., Chen, C.C., 2001. Predictions of flow transitions in a bubble column by chaotic time series analysis of pressure fluctuation signals, Chemical Engineering Science 56 1057-1065.
Lin, T.Y., Tseng, C.H., 2000. Optimum design of artificial neural networks: an example in a bicycle derailleur system, Artificial Intelligence 13 3-14.
Liu, Y.W., Liu, W., Zhang, Y., 2001. Inspection of defects in optical fibers based on back-propagation neural networks, Optics Communications 198(4-6) 369-378.
Ljung, G.M., Box, G.E.P., 1978. On a measure of lack of fit in time series models, Biometrika 65(2) 297-303.
Lorenz, E.N., 1963. Deterministic nonperiodic flow, Journal of Atmospheric Sciences 20(2) 130-141.
Mackey, M.C., Glass, L., 1977. Oscillation and chaos in physiological control systems, Science 197 287-289.
Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen, E., Winkler, R., 1982. The accuracy of extrapolation (time series) method: results of a forecasting competition, Journal of Forecasting 1 111-153.
Maniezzo, V., 1994. Genetic evolution of the topology and weight distribution of neural networks, IEEE Neural Network 5 39-53.
McMillan, A.R., Jones, I.A., Rudd, C.D., Middleton, V., 1998. Statistical study of environmental degradation in resin-transfer moulded structural composites, Science and Manufacturing (Incorporating Composites and Composites Manufacturing) 29 (7) 855-865.
Nelson, P.R., Dudewicz, E.J., 2002. Exact analysis of means with unequal variances, Technometrics 44 (2) 152-160.
Niska, H., Hiltunen, T., Karppinen, A., Ruuskanen, J., Kolehmaxinen, M., 2004. Evolving the neural network model for forecasting air pollution time series, Engineering Applications of Artificial Intelligence 17 159-167.
Nussbaum, M.A., Chaffin, D.B., Martin, B.J., 1995. A back-propagation neural network model of lumbar muscle recruitment during moderate static exertions, Journal of Biomechanics 28(9) 1015-1024.
Perez, C.A., Holzmann, C.A., 1997. Improvements on handwritten digit recognition by genetic selection of neural network topology and by augmented training, IEEE International Conference on Systems Man and Cybernetics 2 1487-1491.
Pitcher, T.J., Buchary, E.A., Hutton, T., 2002. Forecasting the benefits of no-take human-made reefs using spatial ecosystem simulation, Journal of Marine Science 59 S17-S26.
Prybutok, V.R., Yi, J., Mitchell, D., 2000. Comparison of neural network models with ARIMA and regression models for prediction of Houston’s daily maximum ozone concentrations, European Journal of Operational Research 122 31-40.
Radaev, N.N., 1998. Estimate of the number of observations required to check the adequacy of dose-effect models, Atomic Energy 85 (1) 60-65.
Ramasamy, J.V., Rajasekaran, S., 1996. Artificial neural network and genetic algorithm for the design optimization of industrial roofs: a comparison, Computers & Structures 58(4) 747-755.
Reh, L., Ye, H., 2000. Neural networks for on-line prediction and optimization of circulating fluidized bed process steps, Powder Technology 111 123-131.
Roy, R., 1990. A Primer on the Taguchi Method, Van Nostrand Reinhold, New York.
Russell, N.T., Bakker, H.H.C., Chaplin, R.I., 2000. Modular neural network modeling for long-range prediction of an evaporator, Control Engineering Practice 8 49-59.
Saab, S., Badr, E., Nasr, G., 2001. Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon, Energy 26 1-14.
Salchenberger, L.M., Cinar, E.M., Lash, N.A., 1995. Neural networks: a new tool for predicting thrift failures, Decision Sciences 23 899-916.
Samanta, B., Al-Balushi, K.R., Al-Araimi, S.A., 2003. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection, Engineering Applications of Artificial Intelligence 16 657-665.
Seong, J.K., Kwang, S.K., Ho, J., 2003. Optimization of manufacturing parameters for a brake lining using Taguchi method, Journal of Materials Processing Technology 136 202-208.
S exton, R.S., Dorsey, R.E., Johnson, J.D., 1998. Toward global optimization of neural network: a comparison of the genetic algorithm and back propagation, Decision Support Systems 22 171-185.
Sexton, R.S., Dorsey, R.E., Johnson, J.D., 1999. Optimization of neural networks: a comparative analysis of the genetic algorithm and simulated annealing, European Journal of Operational Research 114 589-601.
Srinivas, M., Patnaik, L.M., 1994. Adaptive probabilities of crossover and mutation in genetic algorithms, IEEE Transactions on Systems Man and Cybernetics 24 656-666.
Srivaree-ratana, C., Konak, A., Smith, A.E., 2002. Estimation of all-terminal network reliability using an artificial neural network, Computers & Operations Research 29(7) 849-68.
Sun, M., Stam, A., Steuer, R.E., 2000. Interactive multiple objective programming using Tchebycheff programs and artificial neural networks, Computers & Operations Research 27(7-8) 601-620.
Syrcos, G.P., 2003. Die casting process optimization using Taguchi methods, Journal of Material Processing Technology 135 68-74.
Taguchi, G., 1986. Introduction to Quality Engineering, Asian Productivity Organization, Tokyo.
Takens, F., 1981. Detecting strange attractors in turbulence, In: Rand, D.A., Young, L. S., (Eds.). Dynamical Systems and Turbulence, Springer-Verlag, New York 336-381.
Tan, K.K., Tang, K.Z., 2001. Vehicle dispatching system based on Taguchi-tuned fuzzy rules, European Journal of Operational Research 128 545-557.
Visen, N.S., Paliwal, J., Jayas, D.S., White, N.D.G., 2002. Specialist neural networks for cereal grain classification, Biosystems Engineering 82(2) 151-159.
Wang, T.Y., Huang, C.Y., 2005a. Improving forecasting performance by employing the Taguchi method, European Journal of Operational Research, accepted.
Wang, T.Y., Huang, C.Y., 2005b. Applying optimized BPN to a chaotic time series problem, Expert Systems with Applications, accepted.
Yasumasa, I.K., Hiroaki, Y.S., Masaya, H., Toshikuni, N., Yashio, I., 1994. Application of an improved genetic algorithm to the learning of neural networks, IEICE Transactions on Fundamentals of Electronics, Communication and Computer Science E77-A(4) 731-735.
Zhang, G.P., Patuwo, B.E., Hu, M.Y., 2001. A simulation study of artificial neural networks for nonlinear time-series forecasting, Computers & Operations Research 28(4) 381-96.
Zurada, J.M., 1995. Introduction to Artificial Neural Systems. West Publishing Company, St. Paul.

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