×

Using genetic algorithms grey theory to forecast high technology industrial output. (English) Zbl 1163.91536

Summary: This paper presents an improved method to forecast the output and trends of high technology industry in Taiwan. High technology industry plays a pivotal role in this country’s economy and the social change. The characteristics of high technology industry include rapidly progressive of production techniques, fluctuating market demand, high investment capital, etc. These factors directly affect the difficulty of forecasting trends in this industry. The goal of this study is to overcome these constraints and establish a high-precision forecasting model. To do so, this paper proposes the combined use of grey theory and genetic algorithms. The former is used to forecast the outputs of high tech industry and latter is used to estimate the parameters of a forecasting model based on the minimization of forecasting error. The study uses the example of Taiwan’s integrated circuit industry. Results are very encouraging as this forecasting model clearly helps obtaining precision outcomes. The authors discuss implications of these findings for theory and practice.

MSC:

91B84 Economic time series analysis
91B74 Economic models of real-world systems (e.g., electricity markets, etc.)
68T05 Learning and adaptive systems in artificial intelligence

Software:

Excel
PDF BibTeX XML Cite
Full Text: DOI

References:

[1] Lemola, T., Convergence of national science and technology policies: the case of Finland, Research policy, 1, 8-9, 1481-1490, (2002)
[2] Mathews, J.A., A silicon valley of the east: creating taiwan’s semiconductor industry, California management reviews, 39, 4, 25-54, (1997)
[3] Sher, J.; Yang, Y., The effects of innovative capabilities and R&D clustering on firm performance: the evidence of taiwan’s semiconductor industry, Technovation, 25, 1, 33-43, (2005)
[4] Lee, T.L.; von Tunzelmann, N., A dynamic analytic approach to national innovation systems: the IC industry in Taiwan, Research policy, 34, 4, 425-440, (2005)
[5] L.C. Hsu, C.H. Wang, Forecast the output of integrated circuit industry using a grey model improved by the Bayesian analysis, Technological Forecasting and Social Change, forthcoming (2007).
[6] Deng, J.L., Control problems of grey systems, Systems and control letters, 1, 5, 288-294, (1982) · Zbl 0482.93003
[7] Chen, H.S.; Chang, W.C., A study of optimal grey model GM(1,1), Journal of the Chinese grey system association, 1, 2, 141-145, (1998)
[8] Lin, C.T.; Yang, S.Y., Forecast of the output value of taiwan’s opto-electronics industry using the grey forecasting model, Technological forecasting and social change, 70, 2, 177-186, (2003)
[9] Chang, S.C.; Lai, H.C.; Yu, H.C., A variable P value rolling grey forecasting model for Taiwan semiconductor industry production, Technological forecasting and social change, 72, 5, 623-640, (2005)
[10] Wu, W.Y.; Chen, S.P., A prediction method using the grey model GMC(1,N) combined with the grey relational analysis: a case study on Internet access population forecast, Applied mathematics and computation, 169, 198-217, (2005) · Zbl 1078.68549
[11] Yao, A.W.L.; Chi, S.C., Analysis and design of a Taguchi-grey based electricity demand predictor for energy management systems, Energy conversion and management, 45, 7-8, 1205-1217, (2004)
[12] Huang, Y.F.; Zheng, M.C.; Wu, C.H., Comparison of various different approaches to tourist demand forecasting, Journal of grey system, 7, 1, 21-27, (2004)
[13] Wang, C.H., Predicting the tourism demand using fuzzy time series and hybrid grey theory, Tourism management, 25, 367-374, (2004)
[14] Ong, C.S.; Huang, J.J.; Tzeng, G.H., A novel hybrid model for portfolio selection, Applied mathematics and computation, 169, 2, 1195-1210, (2005) · Zbl 1151.91530
[15] Hsu, C.I.; Wen, Y.U., Improved grey prediction models for trans-Pacific air passenger market, Transportation planning and technology, 22, 87-107, (1998)
[16] Mao, M.; Chirwa, E.C., Application of grey model GM(1,1) to vehicle fatality risk estimation, Technological forecasting and social change, 73, 5, 588-605, (2006)
[17] Jiang, Y.; Yao, Y.; Deng, S.; Ma, Z., Applying grey forecasting to predicting the operating energy performance of air cooled water chillers, International journal of refrigeration, 27, 4, 385-392, (2004)
[18] Lian, R.J.; Lin, B.F.; Huang, J.H., A grey prediction fuzzy controller for constant cutting force in turning, International journal of machine tools and manufacture, 45, 9, 1047-1056, (2005)
[19] Tien, T.L., A research on the prediction of machining accuracy by the deterministic grey dynamic model DGDM(1,1,1), Applied mathematics and computation, 161, 3, 923-945, (2005) · Zbl 1122.93371
[20] Hsu, L.C., Apply the grey prediction model to the global integrated circuit industry, Technological forecasting and social change., 70, 6, 563-574, (2003)
[21] Chang, T.C.; Wen, K.L.; Chang, H.T.; You, M.L., Inverse approach to find an optimal α for grey prediction model, IEEE international conference on system, man and cybernetics, 3, 309-313, (1999)
[22] Holland, J.H., Adaptation in neural and artificial systems, (1975), The University of Michigan Press Ann Arbor
[23] Goldberg, D.E., Genetic algorithms in search, optimization, and machine learning, (1989), Addison-Wesley Publishing Company Massachusetts · Zbl 0721.68056
[24] Kim, A.; Han, I., Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index, Expert systems with applications, 19, 2, 125-132, (2000)
[25] Elalfi, A.E.; Haque, R.; Elalami, M.E., Extracting rules from trained neural network using GA for managing e-business, Applied soft computing, 4, 65-77, (2004)
[26] Fish, K.E.; Johnson, J.D.; Dorsey, R.E.; Blodgett, J.G., Using an artificial neural network trained with a genetic algorithm to model brand share, Journal of business research, 57, 1, 79-85, (2004)
[27] Liu, Z.; Liu, A.; Wang, C.; Niu, Z., Evolving neural network using real coded genetic algorithm for multispectral image classification, Future generation computer system, 20, 1119-1129, (2004)
[28] Niska, H.; Hiltunen, T.; Karppine, A.; Ruuskanen, J.; Kolehmainen, M., Evolving the neural network model for forecasting air pollution time series, Engineering applications of artificial intelligence, 17, 2, 159-167, (2004)
[29] Chang, P.C.; Wang, Y.W.; Tsai, C.Y., Evolving neural network for printed circuit board sales forecasting, Expert systems with applications, 29, 83-92, (2005)
[30] D’heygere, T.; Goethals, P.L.M.; De Pauw, N., Genetic algorithms for optimization of predictive ecosystems models based on decision trees and neural networks, Ecological modelling, 195, 1-2, 20-29, (2006)
[31] Nguyen, H.H.; Chan, C.W., Applications of artificial intelligence for optimization of compressor scheduling, Engineering applications of artificial intelligence, 19, 2, 113-126, (2006)
[32] Zhang, G.Q.; Lai, K.K., Combining path relinking and genetic algorithms for the multiple-level warehouse layout problem, European journal of operational research, 169, 2, 413-425, (2006) · Zbl 1079.90016
[33] Min, S.H.; Lee, J.; Han, I., Hybrid genetic algorithms and support vector machines for bankruptcy prediction, Expert systems with applications, 31, 3, 652-660, (2006)
[34] Kim, K.J., Artificial neural networks with evolutionary instance selection for financial forecasting, Expert systems with applications, 30, 519-526, (2006)
[35] Allen, F.; Karlajainen, R., Using genetic algorithms to find technical trading rules, Journal of financial economics, 51, 245-271, (1999)
[36] Ong, C.S.; Huang, J.J.; Tzeng, G.H., Model identification of ARIMA family using genetic algorithms, Applied mathematics and computation, 164, 885-912, (2005) · Zbl 1070.65005
[37] Shin, T.; Han, I., Optimal signal multi-resolution by genetic algorithms to support artificial neural networks for exchange-rate forecasting, Expert systems with applications, 18, 4, 257-269, (2000)
[38] Mitra, S.; Mitra, A., Modeling exchange rates using wavelet decomposed genetic neural networks, Statistical methodology, 3, 2, 103-124, (2006) · Zbl 1248.91077
[39] Leigh, W.; Purvis, R.; Ragusa, J.M., Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support, Decision support systems, 32, 4, 361-377, (2002)
[40] Jeong, B.; Jung, H.S.; Park, N.K., A computerized causal forecasting system using genetic algorithms in supply chain management, Journal of systems and software, 60, 3, 223-237, (2002)
[41] Pai, P.F., System reliability forecasting by support vector machines with genetic algorithms, Mathematical and computer modelling, 43, 3-4, 262-274, (2006) · Zbl 1187.90113
[42] Pai, P.F.; Hong, W.C., Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms, Electric power systems research, 74, 3, 417-425, (2005)
[43] T. Back, The interaction of mutation rate, selection, and self-adaptation within a genetic algorithm, in: Proceedings of the Second Conference on Parallel Problem Solving for Nature, 1992.
[44] Srinivas, M.; Patnaik, L.M., Genetic algorithms: a survey, IEEE computer, 276, 17-26, (1994)
[45] Lewis, C., Industrial and business forecasting methods, (1982), Butterworth Scientific London
[46] Palisade Corporation, Guide to Evolver: The Genetic Algorithm Solver for Microsoft Excel, Newfield, New York, 2001.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.