Polynomial neural networks architecture: Analysis and design

Computers and Electrical Engineering

Volumn 29, Issue 6, 2003, Pages 703-725

  Oh, Sung Kwun ^a   Pedrycz, Witold ^b,c   Park, Byoung Jun ^a  

^a Wonkwang University   (South Korea)

^b UNIVERSITY OF ALBERTA   (Canada)

^c SYSTEMS RESEARCH INSTITUTE   (Poland)

Author keywords

Design procedure; Group method of data handling (GMDH); Polynomial neural networks (PNNs); Self organizing network; The generic and the advanced type of PNNs

Indexed keywords

DATA REDUCTION; NEURAL NETWORKS; NONLINEAR SYSTEMS; POLYNOMIALS;

NEURAL NETWORKS ARCHITECTURE;

SYSTEMS ANALYSIS;

EID: 0038722717     PISSN: 00457906     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0045-7906(02)00045-9     Document Type: Article

References (18)

1. Ivahnenko A.G. Polynomial theory of complex systems. IEEE Trans. Systems Man Cybernet. SMC-1:1971;364-378.
2. Ivahnenko A.G., Madala H.R. Inductive learning algorithms for complex systems modeling. 1994;CRC Press, London.
3. Steiger D.M., Sharda R. Analyzing mathematical models with inductive learning networks. European J. Oper. Res. 93:1996;387-401.
4. Pachepsky Ya., Rawls W., Gimenez D., Watt J.P.C. Use of soil penetration resistance and group method of data handling to improve soil water retention estimates. Soil Tillage Res. 49:1998;117-126.
5. Farlow S.J. The GMDH algorithm. Farlow S.J. Self-organizing methods in modeling: GMDH type algorithms. 1984;1-24 Marcel Dekker, New York.
6. Nakanishi H., Turksen I.B., sugeno M. A review and comparison of six reasoning methods. Fuzzy Sets and Systems. 57:1992;257-294.
7. Sugeno M., Yasukawa T. A fuzzy-logic-based approach to qualitative modeling. IEEE Trans. Fuzzy Systems. 1(1):1993;7-31.
8. Box G.E.P., Jenkins F.M. Time series analysis: Forecasting and control. 2nd ed. 1976;Holden-day.
9. Xu C.W., Zailu Y. Fuzzy model identification self-learning for dynamic system. IEEE Trans. Systems Man Cybernet. SMC-17(4):1987;683-689.
10. Pedrycz W. An identification algorithm in fuzzy relational system. Fuzzy Sets and Systems. 13:1984;153-167.
11. Lin Y., Cunningham G.A. A new approach to fuzzy-neural modeling. IEEE Trans. Fuzzy Systems. 3(2):1995;190-197.
12. Oh S.K., Pedrycz W. Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems. Fuzzy Sets and Systems. 115(2):2000;205-230.
13. Park CS, Oh SK, Pedrycz W. Fuzzy identification by means of auto-tuning algorithm and weighting factor. The Third Asian Fuzzy Systems Symposium (AFSS) 1998;701-6.
14. Oh SK, Ahn TC, Pedrycz W. Fuzzy polynomial neural networks-based architecture and its application to nonlinear process systems. Seventh IFSA World Congress, Prague 1997;495-9.
15. Oh S.K., Yoon K.C., Kim H.K. The design of optimal fuzzy-neural networks architecture by means of GA and an aggregate weighted performance index. J. Control Automation Systems Engrg. 6(3):2000;. [in Korean].
16. Kim E.T., Park M.K., Ji S.H., Park M. A new approach to fuzzy modeling. IEEE Trans. Fuzzy Systems. 5(3):1997;328-337.
17. Kim E., Lee H., Park M., Park M. A simple identified Sugeno-type fuzzy model via double clustering. Inform. Sci. 110:1998;25-39.
18. Gomez-Skarmeta A.F., Delgado M., Vila M.A. About the use of fuzzy clustering techniques for fuzzy model identification. Fuzzy Sets and Systems. 106:1999;179-188.