Advance Neuro-Fuzzy Modeling Using a New Clustering Algorithm

새로운 클러스터링 알고리듬을 적용한 향상된 뉴로-퍼지 모델링

  • Published : 2004.07.01

Abstract

In this paper, we proposed a new method of modeling a neuro-fuzzy system using a hybrid clustering algorithm. The initial parameters and the number of clusters of the proposed system are optimally chosen simultaneously with respect to the process of regression, which is a unique characteristics of the proposed system. The proposed algorithm presented in this work improves the overall performance of the proposed a neuro-fuzzy system by choosing a proper number of clusters adaptively according the characteristics of given data. The process of clustering is performed by deciding on the number of classes, which yields the property of convergence of the system. In experiments, the superiority of the proposed neuro-fuzzy system is demonstrated, especially the process of optimizing parameters and clustering of learning speed.

Keywords

References

  1. C. T. Lin, C. S. G. LEE, Neural Fuzzy System : A Neuro-Fuzzy Synergism to Intelligent Systems, Prentice Hall, 1996
  2. J. S. R. Jang, C. T. Sun, E. M. Mizutani, Neuro-Fuzzy and Soft Computing : A Computational Approach to Learning and Machine Intelligence, Prentice Hall, 1997
  3. J. S. R. Jang, 'ANFIS : Adaptive Network-based Fuzzy Inference System', IEEE Trans. on System, Man, and Cybernetics, Vol.23, No. 3, pp. 665-685, 1993 https://doi.org/10.1109/21.256541
  4. 김승석, 곽근창, 유정웅, 전명근, 'GMM과 클러스터링 기법에 의한 뉴로-퍼지 시스템 모델링', 한국 퍼지 및 지능시스템 학회 논문지, vol. 12, No. 6, pp. 571-576, 2002
  5. S. K. Oh, Witold Predrycz, 'Identification of Fuzzy system by means of an auto-tuning algorithm and its application to nonlinear systems', Fuzzy Sets and Systems, Vol. 115, pp. 205-230, 2000 https://doi.org/10.1016/S0165-0114(98)00174-2
  6. Witold Pedrycz, 'Conditional Fuzzy C-Means', Pattern Recognition Letters, Vol. 17, Issue. 6, pp. 625-631, 1996 https://doi.org/10.1016/0167-8655(96)00027-X
  7. B. J. Park, W. Pedrycz, S. K. Oh, 'Identification of fuzzy models with the aid of evolutionary data granulation', Control Theory and Applications, IEE Proceedings, Vol. 148, Issue. 5, pp. 406-418, 2001 https://doi.org/10.1049/ip-cta:20010677
  8. Roy L. Streit, Tod E. Luginbuhl, 'Mazimum Likelihood training of Probabilitic Neural Networks', IEEE Trans. on Neural Networks, Vol. 5, No. 5, pp. 764-782, 1994 https://doi.org/10.1109/72.317728
  9. Guorong Xuan, Wei Zhang, Peiqi Chai, 'Em algorithm of Gaussian Mixture Model and Hidden Markov Model', Image Processing Proceedings, International Conference on, Vol. 1, pp. 145-148, 2001 https://doi.org/10.1109/ICIP.2001.958974
  10. Ethem Alpayd, 'Soft Vector Quantization and the EM algorithm', Neural Network, vol. 11, Issue. 3, pp. 467-477, 1998 https://doi.org/10.1016/S0893-6080(97)00147-0
  11. Xiangyu Yang, Jun Liu, 'Unsupervised Learning of Finite Mixture Models', Pattern Recognition Letters, Vol. 23, Issue. 5, pp. 501-502, 2002 https://doi.org/10.1109/34.990138
  12. Ching-Chang Wong, Chia-Chong Chen, Mu-Chun Su, 'A novel algorithm for data clustering', Pattern Recognition', Vol. 34, Issue. 2, pp. 425-442, 2001 https://doi.org/10.1016/S0031-3203(00)00002-9
  13. 김승석, 곽근창, 유정웅, 전명근, '계층적 클러스터링과 Gaussian Mixture Model을 이용한 뉴로-퍼지 모델링', 한국 퍼지 및 지능시스템 학회 논문지, Vol. 13, No. 5, pp. 512-519, 2003
  14. S. S. Kim, K. C. Kwak, S. S. Kim, J. W. Ryu, M. G. Chun, 'A Novel Neuro-Fuzzy Modeling using the Gaussian Mixture Model', ICCAS 2002, 2002
  15. J. S. R. Jang, 'Input selection for ANFIS learning', Fuzzy Systems, Proceedings of the Fifth IEEE International Conference on, Vol. 2, pp. 1493-1499, 1996 https://doi.org/10.1109/FUZZY.1996.552396