Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons

Information Sciences

Volumn 176, Issue 23, 2006, Pages 3490-3519

  Oh, Sung Kwun ^a   Pedrycz, Witold ^b,c   Roh, Seok Beom ^d  

^a Suwon University   (South Korea)

^b UNIVERSITY OF ALBERTA   (Canada)

^c SYSTEMS RESEARCH INSTITUTE   (Poland)

^d Wonkwang University   (South Korea)

Author keywords

Fuzzy polynomial neuron (FPN); Fuzzy set based polynomial neuron (FSPN); Genetic algorithms (GAs); Genetically optimized fuzzy polynomial neural networks (gFPNN); Group method of data handling (GMDH); Multi layer perceptron (MLP); Self organizing network

Indexed keywords

COMPUTER ARCHITECTURE; FUZZY SETS; GENETIC ALGORITHMS; LEAST SQUARES APPROXIMATIONS; MULTILAYER NEURAL NETWORKS; OPTIMAL CONTROL SYSTEMS; POLYNOMIAL APPROXIMATION;

FUZZY POLYNOMIAL NEURON (FPN); FUZZY SET-BASED POLYNOMIAL NEURON (FSPN); GENETICALLY OPTIMIZED FUZZY POLYNOMIAL NEURAL NETWORKS (GFPNN); GROUP METHOD OF DATA HANDLING (GMDH); SELF-ORGANIZING NETWORK;

NEURAL NETWORKS;

EID: 33748205931     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2005.11.009     Document Type: Article

References (29)

1. Cherkassky V., Gehring D., and Mulier F. Comparison of adaptive methods for function estimation from samples. IEEE Trans. Neural Networks 7 July (1996) 969-984
2. Cordón O., Gomide F., Herrera F., Hoffmann F., and Magdalena L. Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets Systems 141 1 (2004) 5-31
3. Crowder III R.S. Predicting the Mackey-Glass time series with cascade-correlation learning. In: Touretzky D., Hinton G., and Sejnowski T. (Eds). Proceedings of the 1990 Connectionist Models Summer School (1990), Carnegie Mellon University 117-123
4. Dickerson J.A., and Kosko B. Fuzzy function approximation with ellipsoidal rules. IEEE Trans. Syst., Man, Cybernet., Part B 26 Aug (1996) 542-560
5. Gomez-Skarmeta A.F., Delgado M., and Vila M.A. About the use of fuzzy clustering techniques for fuzzy model identification. Fuzzy Sets Systems 106 (1999) 179-188
6. Jang J.S.R. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. System, Man, Cybern. 23 3 (1993) 665-685
7. Jong D. Are genetic algorithms function optimizers?. In: Manner R., and Manderick B. (Eds). Parallel Problem Solving from Nature vol. 2 (1992), North-Holland, Amsterdam 3-14
8. Kim E.-T., et al. A new approach to fuzzy modeling. IEEE Trans. Fuzzy Syst. 5 3 (1997) 328-337
9. Kim E.-T., et al. A simple identified Sugeno-type fuzzy model via double clustering. Informat. Sci. 110 (1998) 25-39
10. Kleinsteuber S., and Sepehri N. A polynomial network modeling approach to a class of large-scale hydraulic systems. Comput. Electron. Eng. 22 (1996) 151-168
11. A.S. Lapedes, R. Farber, Non-linear signal processing using neural networks: prediction and system modeling, Technical Report LA-UR-87-2662, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, 1987.
12. Li C.J., and Huang T.-Y. Automatic structure and parameter training methods for modeling of mechanical systems by recurrent neural networks. Appl. Math. Model. 23 (1999) 933-944
13. In: Lyu M.R. (Ed). Handbook of Software Reliability Engineering (1995), McGraw-Hill, New York
14. Mackey M.C., and Glass L. Oscillation and chaos in physiological control systems. Science 197 (1977) 287-289
15. Maguire L.P., Roche B., McGinnity T.M., and McDaid L.J. Predicting a chaotic time series using a fuzzy neural network. Informat. Sci. 112 (1998) 125-136
16. Michalewicz Z. Genetic Algorithms + Data Structures = Evolution Programs (1996), Springer-Verlag, Berlin, Heidelberg
17. Oh S.-K., Kim D.-W., Park B.-J., and Hwang H.-S. Advanced polynomial neural networks architecture with new adaptive nodes, transactions on control. Automat. Syst. Eng. 3 1 (2001) 43-50 (in Korean)
18. Oh S.-K., and Pedrycz W. The design of self-organizing polynomial neural networks. Informat. Sci. 141 (2002) 237-258
19. S.-K. Oh, W. Pedrycz, B.-J. Park, T.-C. Ahn, "A hybrid modeling architecture for software engineering, in: Proceedings of the 6th IASTED International Conference ASC, July 2002, pp. 309-314.
20. Oh S.-K., Pedrycz W., and Ahn T.-C. Self-organizing neural networks with fuzzy polynomial neurons. Appl. Soft Comput. 2 1 (2002) 1-10
21. Oh S.-K., and Pedrycz W. Fuzzy polynomial neuron-based self-organizing neural networks. Int. J. General Syst. 32 3 (2003) 237-250
22. Oh S.-K., Pedrycz W., and Park B.-J. Polynomial neural networks architecture: analysis and design. Comput. Elect. Eng. 29 6 (2003) 703-725
23. Oh S.-K., and Pedrycz W. Self-organizing polynomial neural networks based on PNS or FPNS: analysis and design. Fuzzy Sets Syst. 142 2 (2004) 163-198
24. Oh S.-K., Pedrycz W., and Park B.-J. Relation-based neurofuzzy networks with evolutionary data granulation. Math. Comput. Modell. 40 7-8 (2004) 891-921
25. Park B.-J., Pedrycz W., and Oh S.-K. Fuzzy Polynomial neural networks : hybrid architectures of fuzzy modeling. IEEE Trans. Fuzzy Syst. 10 5 (2002) 607-621
26. Sommer V., Tobias P., Kohl D., Sundgren H., and Lundstrom L. Neural networks and abductive networks for chemical sensor signals: A case comparison. Sensors Actuators B 28 (1995) 217-222
27. Sugeno M., and Yasukawa T. A fuzzy-logic-based approach to qualitative modeling. IEEE Trans. Fuzzy Syst. 1 1 (1993) 7-31
28. Wang L.X., and Mendel J.M. Generating fuzzy rules from numerical data with applications. IEEE Trans. Syst., Man, Cybernet. 22 6 (1992) 1414-1427
29. Zadeh L.A. Toward a generalized theory of uncertainty (GTU)-an outline. Inform. Sci. 172 1-2 (2005) 1-40