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The Analysis and Design of Advanced Neurofuzzy Polynomial Networks  

Park, Byeong-Jun (원광대학교 전기전자·정보공학부)
O, Seong-Gwon (원광대학교 전기전자·정보공학부)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.39, no.3, 2002 , pp. 18-31 More about this Journal
Abstract
In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.
Keywords
Neurofuzzy networks(NFN); Polynomial neural networks(PNN); Advanced neurofuzzy polynomial networks(ANFPN); Computational Intelligence(Cl); Group method of data handling(GMDH); Genetic algorithms (GAs);
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Times Cited By KSCI : 4  (Citation Analysis)
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1 A.G. Ivahnenko, 'The group method of data handing : a rival of method of stochastic approximation,' Soviet Automatic Control, Vol. 13, No. 3, pp. 43-55, 1968
2 Shin ichi Horikawa, Takeshi Furuhashi and Yoshiki Uchigawa, 'On Fuzzy Modeling Using Fuzzy Neural Networks with the Back Propagation Algorithm,' IEEE trans. Neural Networks, Vol. 3, No. 5, pp. 801-806, 1992   DOI   ScienceOn
3 N. Imasaki, J. Kiji and T. Endo, 'A Fuzzy Rule Structured Neural Networks,' Journal of Japan Society for Fuzzy Theory and Systems, Vol. 4, No. 5, pp. 985-995, 1992(in Japanese)
4 H. Nomura and Wakami, 'A Self-Tuning Method of Fuzzy Control by Descent Methods,' 4th IFSA World Conference, pp. 155-159, 1991
5 M. Sugeno and T. Yasukawa, 'A Fuzzy-Logic-Based Approach to Qualitative Modeling,' IEEE Trans. on Fuzzy systems, Vol. 1, No. 1, pp. 7-31, 1993   DOI   ScienceOn
6 A.F. Gomez Skarmeta, M. Delgado and M.A. Vila, 'About the use of fuzzy clustering techniques for fuzzy model identification,' Fuzzy Sets and Systems, Vol. 106, pp. 179-188, 1999   DOI   ScienceOn
7 H. Takagi and I. Hayashi, 'NN-driven Fuzzy Reasoning,' Int. J. of Approximate Reasoning, Vol. 5, No. 3, pp. 191-212, 1991   DOI   ScienceOn
8 Y. Lin and G.A. Cunningham III, 'A new approach to fuzzy-neural modeling,' IEEE Trans. on Fuzzy Systems, Vol. 3, No. 2, pp. 190-197, 1995   DOI   ScienceOn
9 E. Kim, H. Lee, M. Park and M. Park, 'A simply identified Sugeno-type fuzzy model via double clustering,' Information Sciences, Vol. 110, pp. 25-39, 1998   DOI   ScienceOn
10 G.E. Box and G.M. Jenkins, Time Series Analysis : Forecasting and Control, Holden-day, 1970
11 S.K. Oh and W. Pedrycz, 'Fuzzy Identification by Means of Nonlinear Systems,' Fuzzy Sets and Syst., Vol. 115, No. 2, pp. 205-230, 2000   DOI   ScienceOn
12 Takeshi Yamakawa, 'A New Effectie Learning Algorithm for a Neo Fuzzy Neuron Model,' 5th IFSA World Conference, pp. 1017-1020, 1993
13 오성권, 박병준, 박춘성, '적응 퍼지-뉴럴네트워크를 이용한 비선형 공정의 온라인 모델링,' 대한전기학회논문지, 제48A권, 제10호, pp. 1293-1302, 1999   과학기술학회마을
14 박병준, 오성권, 안태천, 김현기, '유전자 알고리즘과 하중값을 이용한 퍼지 시스템의 최적화,' 대한전기학회논문지, 제48A권, 제6호, pp. 789-799, 1999   과학기술학회마을
15 오성권, 김동원, 박병준, '다항식 뉴럴네트워크 구조의 최적 설계에 관한 연구,' 대한전기학회논문지, 제49D권, 3호, pp. 145-156, 2000   과학기술학회마을
16 E.T. Kim, M.K. Park, S.H. Ji and M. Park, 'A new approach to fuzzy modeling,' IEEE Trans. on Fuzzy systems, Vol. 5, No. 3, pp. 328-337, 1997   DOI   ScienceOn
17 David E. Goldberg, Genetic Algorithms in search, Optimization & Machine Learning, Addison-wesley, 1989
18 Zbigniwe Michalewicz, Genetic Algorithms+Data Structue=Evolution Programs, Springer-Verlag, 1992