참고문헌
- V. Cherkassky, D. Gehring, F. Mulier, 'Comparison of adaptive methods for function estimation from samples', IEEE Trans. Neural Networks, Vol. 7, pp. 969 984, July, 1996 https://doi.org/10.1109/72.508939
- J. A. Dickerson and B. Ksoko, 'Fuzzy function approximation with ellipsoidal rules', IEEE Trans. Syst., Man, Cybernetics, Part B, Vol. 26, pp.542 560, Aug., 1996 https://doi.org/10.1109/3477.517030
- A. G. Ivakhnenko, 'Polynomial theory of complex systems', IEEE Trans. on System, Man and Cybernetics, Vol. SMC-1, pp. 364-378, 1971
- A. G. Ivakhnenko and H. R. Madala, 'Inductive Learning Algorithms for Complex Systems Modeling', CRC Press, Boca Raton, Fl, 1994
- S.-K. Oh, W. Pedrycz, 'The design of self-organizing Polynomial Neural Networks', Information Science, Vol. 141, pp. 237-258, 2002 https://doi.org/10.1016/S0020-0255(02)00175-5
- 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. 703-725, 2003 https://doi.org/10.1016/S0045-7906(02)00045-9
- C. W. Xu, and Y. Zailu, 'Fuzzy model identification self-learning for dynamic system', IEEE Trans. on Syst. man, Cybern., Vol. SMC-17, No.4, pp.683-689, 1987 https://doi.org/10.1109/TSMC.1987.289361
- W. Pedrycz, 'An identification algorithm in fuzzy relational system', Fuzzy Sets Syst., Vol. 13, pp.153-167, 1984 https://doi.org/10.1016/0165-0114(84)90015-0
- S.-K. Oh and W. Pedrycz, 'Fuzzy Polynomial Neuron-based Self-Organizing Neural Networks', Int. J. of General Systems, Vol. 32, No. 3, pp. 237-250, May, 2003 https://doi.org/10.1080/0308107031000090756
- S.-K. Oh, W. Pedrycz and T.-C. Ahn, 'Selforganizing neural networks with fuzzy polynomial neurons', Applied Soft Computing, Vol. 2, Issue 1F, pp. 1-10, Aug. 2002 https://doi.org/10.1016/S1568-4946(02)00023-6
- S.-K. Oh, W. Pedrycz, H.-S. Park, 'Self-organizing Networks in Modeling Experimental Data in Software Engineering', IEE Proc.-Computers and Digital Techniques, Vol. 149, Issue 3, pp. 61-78, May, 2002 https://doi.org/10.1049/ip-cdt:20020411
- S.-K. Oh and D.-Y. Lee, 'Advanced Self-organizing Neural Networks with Fuzzy Polynomial Neurons: Analysis and Design', KIEE International Transactions on Systems and Control(SC), Vol. 12D, No. 1, pp. 12-16, March, 2002
- Holland, J. H., Adaptation In Natural and Artificial Systems, The University of Michigan Press, Ann Arbour. 1975
- D. E. Goldberg, Optimization & Machine Learning,Genetic Algorithm in search, Addison wesley, 1989
- K. De Jong. Are genetic algorithms function optimizers? In Proc. of PPSN Ⅱ(Parallel Problem Solving from Nature), pages 3-13, Amsterdam, North Holland, 1992
- D. E. Box and G. M. Jenkins, Time Series Analysis, Forcasting and Control, California: Holden Day, 1976
- M. Sugeno and T. Yasukawa, 'Linguistic modeling based on numerical data', IFSA 91, Brussels, Computer, Management & Systems Science, pp. 264-267, 1991
- J. Q. Chen, Y. G. Xi and Z.J. Zhang, 'A clustering algorithm for fuzzy model identification', Fuzzy Sets and Systems, Vol. 98, pp. 319-329, 1998 https://doi.org/10.1016/S0165-0114(96)00384-3
- 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 https://doi.org/10.1016/S0165-0114(97)00276-5
- S.-K. Oh and W. Pedrycz, 'Identification of Fuzzy Systems by means of an Auto-Tuning Algorithm and Its Application to Nonlinear Systems', Fuzzy sets and Systems, Vol. 115, No. 2, pp. 205-230, 2000 https://doi.org/10.1016/S0165-0114(98)00174-2
- E.-T. Kim, et al, 'A new approach to fuzzy modeling', IEEE Trans. Fuzzy Systems', IEEE Trans. Fuzzy Systems, Vol. 5, No. 3, pp. 328-337, 1997 https://doi.org/10.1109/91.618271
- E.-T. Kim, et al, 'A simple identified Sugeno-type fuzzy model via double clustering', Infromation Science, Vol. 110, pp. 25-39, 1998 https://doi.org/10.1016/S0020-0255(97)10083-4
- J. Leski and E. Czogala, 'A new artificial neural networks based fuzzy inference system with moving consequents in if-then rules and selected applications', Fuzzy Sets and Systems, Vol. 108, pp. 289-297, 1999 https://doi.org/10.1016/S0165-0114(97)00314-X
- Y. Lin, G. A. Cunningham Ⅲ, 'A new approach to fuzzy-neural modeling', IEEE Trans. Fuzzy Systems, Vol. 3, No. 2, pp. 190-197, 1995 https://doi.org/10.1109/91.388173
- Yin Wang and Gang Rong, 'A self-organizing neural-network-based fuzzy system', Fuzzy Sets and Systems, Vol. 103, pp. 1-11, 1999 https://doi.org/10.1016/S0165-0114(97)00196-6
- H.-S. Park, S.-K. Oh, Y.-W. Yoon, 'A New Modeling Approach to Fuzzy-Neural Networks Architecture', Journal of Control, Automation and Systems Engineering, Vol. 7, No. 8, pp. 664-674
- S.-K. Oh, D.-W. Kim and B.-J. Park, 'A Study on the Optimal Design of Polynomial Neural Networks Structure', The Transactions of The Korean Institute of Electrical Engineers, Vol. 49D, No. 3, pp. 145-156
- S.-K. Oh, W. Pedrycz and D.-W. Kim, 'Hybrid Fuzzy Polynomial Neural Networks', Int. J. of Uncertainty, fuzziness and Knowledge-Based Systems, Vol. 10, No. 3, pp. 257-280, June, 2002 https://doi.org/10.1142/S0218488502001478
- L. X. Wang, J. M. Mendel, 'Generating fuzzy rules from numerical data with applications', IEEE Trans. Systems, Man, Cybern., Vol. 22, No. 6, pp. 1414-1427, 1992 https://doi.org/10.1109/21.199466
- J. S. R. Jang, 'ANFIS: Adaptive-Network-Based Fuzzy Inference System', IEEE Trans. System. Man. and Cybern., Vol. 23, No. 3, pp. 665-685, 1993 https://doi.org/10.1109/21.256541
- L. P. Maguire, B. Roche, T. M. McGinnity, L. J. McDaid, 'Predicting a chaotic time series using a fuzzy neural network', Information Sciences, Vol. 112, pp. 125-136, 1998 https://doi.org/10.1016/S0020-0255(98)10026-9
- C. James Li, T.-Y. Huang, 'Automatic structure and parameter training methods for modeling of mechanical systems by recurrent neural networks', Applied Mathematical Modeling, Vol. 23, pp. 933-944, 1999 https://doi.org/10.1016/S0307-904X(99)00020-7
- A. S. Lapedes and R. Farber, 'Nonlinear signal processing using neural networks: prediction and systems modeling', Technical Report LA-UR-87-2662, Los Alamos National Laboratory, Los Alamos, New mexico 87545, 1987
- 오성권, 'C 프로그래밍에 의한 퍼지모델 및 제어시스템', 내하출판사, 2002. 1
- 오성권, '프로그래밍에 의한 컴퓨터지능(퍼지, 신경회로망및 유전자알고리즘을 중심으로)', 내하출판사, 2002. 8