Browse > Article
http://dx.doi.org/10.5391/JKIIS.2005.15.2.236

Evolutionally optimized Fuzzy Polynomial Neural Networks Based on Fuzzy Relation and Genetic Algorithms: Analysis and Design  

Park, Byoung-Jun (원광대학교 전기전자 및 정보공학부)
Lee, Dong-Yoon (중부대학교 정보통신공학부)
Oh, Sung-Kwun (수원대학교 전기공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.15, no.2, 2005 , pp. 236-244 More about this Journal
Abstract
In this study, we introduce a new topology of Fuzzy Polynomial Neural Networks(FPNN) that is based on fuzzy relation and evolutionally optimized Multi-Layer Perceptron, discuss a comprehensive design methodology and carry out a series of numeric experiments. The construction of the evolutionally optimized FPNN(EFPNN) exploits fundamental technologies of Computational Intelligence. The architecture of the resulting EFPNN results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks(PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the EFPNN. The consequence part of the EFPNN is designed using PNN. As the consequence part of the EFPNN, the development of the genetically optimized PNN(gPNN) dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the EFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed EFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.
Keywords
Evolutionally optimized Fuzzy Polynomial Neural Networks (EFPNN); Multi-Layer Perceptron(MLP); Computational Intelligence(CI); Fuzzy Relation-based Fuzzy Neural Networks(FNN); genetically optimized PNN(gPNN);
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 B. J. Park, W. Pedrycz and S. K. Oh, 'Fuzzy Polynomial Neural Networks: Hybrid Architectures of Fuzzy Modeling', IEEE Transaction on Fuzzy Systems, Vol. 10, Issue 5, pp. 607-621, 2002   DOI   ScienceOn
2 S. K. Oh, W. Pedrycz and B. J. Park, 'Self-organizing Neurofuzzy Networks Based on Evolutionary Fuzzy Granulation', IEEE Transaction on Systems, Man and Cybernetics-part A, Vol. 33, No. 2, pp. 271-277, 2003
3 오성권, 프로그래밍에 의한 컴퓨터지능(퍼지, 신경 회로망 및 진화알고리즘을 중심으로), 내하출판사, 2002
4 S. I. Horikawa, T. Furuhashi and Y. Uchigawa, 'On Fuzzy Modeling Using Fuzzy Neural Networks with the Back Propagation Algorithm', IEEE Transactions on Neural Networks, Vol. 3, No. 5, pp. 801-806, 1992   DOI   ScienceOn
5 David E. Goldberg, Genetic Algorithms in search, Optimization&Machine Learning, Addison-wesley, 1989
6 Z.Michalewicz, Genetic Algorithms+Data Structure =Evolution Programs, Springer-Verlag, 1992
7 T. Kondo, 'Revised GMDH algorithm estimating degree of the complete polynomial', Transactions of the Society of Instrument and Control Engineers, Vol. 22, No. 9, pp. 928-934, 1986
8 안태천, 오성권, '발전소의 대기오염물질 배출패턴 모델정립', 기초전력공학 공동연구소, 1997
9 S. K. Oh, W. Pedrycz and H. S. Park, 'Hybrid Identification in Fuzzy-Neural Networks', Fuzzy Sets and Systems, Vol. 138, pp. 399-426, 2003   DOI   ScienceOn
10 H. S. Park and S. K. Oh, 'Multi-FNN Identification Based on HCM Clustering and Evolutionary Fuzzy Granulation', International Journal of Control, Automation and Systems, Vol. 1, No. 2, pp. 194-202, 2003
11 管野道夫(譯:박민용,최항식), 퍼지제어 시스템, pp. 143-158, 대영사, 1990
12 S. K. Oh, W. Pedrycz and B. J. Park, 'Polynomial Neural Networks Architecture: Analysis and Design', Computers and Electrical Engineering, Vol. 29, Issue 6, pp. 653-725, 2003   DOI   ScienceOn
13 S. K. Oh and W. Pedrycz, 'Fuzzy Identification by Means of Auto- Tuning Algorithm and Its Application to Nonlinear Systems', Fuzzy Sets and Systems, Vol. 115, No. 2, pp. 205-230, 2000   DOI   ScienceOn
14 K. S. Narendra and K. Parthasarathy, 'Gradient Methods for the Optimization of Dynamical Systems Containing Neural Networks', IEEE Transactions on Neural Networks, Vol. 2, pp. 252 262, 1991   DOI   ScienceOn
15 A. G. Ivakhnenko, 'The Group Method of Data Handling; a Rival of Method of Stochastic Approximation', Soviet Automatic Control, Vol. 1, No. 3, pp. 43-55, 1968
16 G. Kang and M. Sugeno, 'Fuzzy Modeling', Transactions of the Society of Instrument and Control Engineers, Vol. 23, No. 6, pp. 106-108, 1987
17 W. Pedrycz and J. F. Peters, Computational Intelligence and Software Engineering, World Scientific, Singapore, 1998
18 S. K. Oh and W. Pedrycz, 'The Design of Self-organizing Polynomial Neural Networks', Information Sciences, Vol. 141, Issue 3-4, pp. 237- 258, 2002   DOI   ScienceOn