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Neural Computing and Applications

, Volume 18, Issue 6, pp 633–641 | Cite as

An stable online clustering fuzzy neural network for nonlinear system identification

  • José de Jesús Rubio
  • Jaime Pacheco
Original Article

Abstract

In this paper, we propose a online clustering fuzzy neural network. The proposed neural fuzzy network uses the online clustering to train the structure, the gradient to train the parameters of the hidden layer, and the Kalman filter algorithm to train the parameters of the output layer. In our algorithm, learning structure and parameter learning are updated at the same time, we do not make difference in structure learning and parameter learning. The center of each rule is updated to obtain the center is near to the incoming data in each iteration. In this way, it does not need to generate a new rule in each iteration, i.e., it neither generates many rules nor need to prune the rules. We prove the stability of the algorithm.

Keywords

Fuzzy neural networks Clustering Nonlinear systems Identification Stability 

References

  1. 1.
    Azem MF, Hanmandlu M, Ahmad N (2003) Structure identification of generalized adaptive neuro-fuzzy inference systems. IEEE Trans Fuzzy Syst 11(6):666–681CrossRefGoogle Scholar
  2. 2.
    Brown M, Harris CJ (1994) Adaptive Modelling and Control. Macmillan Pub.Co., Prentice Hall, New YorkGoogle Scholar
  3. 3.
    Chiu SL (1994) Fuzzy model Identification based on cluster estimation. J Intell Fuzzy Syst 2(3):267–278Google Scholar
  4. 4.
    Hilera JR, Martines VJ (1995) Redes Neuronales Artificiales, Fundamentos, Modelos y Aplicaciones. Adison Wesley Iberoamericana, USAGoogle Scholar
  5. 5.
    Jang JSR (1993) AFNFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybernet 23:665–685CrossRefGoogle Scholar
  6. 6.
    Jang JSR, Sun CT (1997) Neuro-fuzzy and soft computing. Prentice Hall, USA, p 07458Google Scholar
  7. 7.
    Juang CF, Lin CT (1998) An on-line self constructing neural fuzzy inference network and its applications. IEEE Trans Fuzzy Syst 6(1):12–32CrossRefGoogle Scholar
  8. 8.
    Juang CF, Lin CT (1999) A recurrent self-organizing fuzzy inference network. IEEE Trans Neural Netw 10(4):828–845CrossRefGoogle Scholar
  9. 9.
    Kasabov N (2001) Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning. IEEE Trans Syst Man Cybernet 31(6):902–918CrossRefGoogle Scholar
  10. 10.
    Lin CT (1994) Neural fuzzy control systems with structure and parameter learning. World Scientific, New YorkGoogle Scholar
  11. 11.
    Mitra S, Hayashi Y (2000) Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Trans Neural Netw 11(3):748–769CrossRefGoogle Scholar
  12. 12.
    Rivals I, Personnaz L (2003) Neural network construction and selection in non linear modelling. IEEE Trans Neural Netw 14(4):804–820CrossRefGoogle Scholar
  13. 13.
    Rubio JJ, Yu W (2005) Dead-zone Kalman filter algorithm for recurrent neural networks. 44th IEEE Conference on Decision and Control, Spain, pp 2562–2567Google Scholar
  14. 14.
    Rubio JJ, Yu W (2006) A new discrete-time sliding mode control with time-varing gain and neural identification. J Control 79(4):2562–2567Google Scholar
  15. 15.
    Tzafestas SG, Zikidis KC (2001) On-line neuro-fuzzy ART-based structure and parameter learning TSK model. IEEE Trans Syst Man Cybernet 31(5):797–803CrossRefGoogle Scholar
  16. 16.
    Wang LX (1997) A course in fuzzy systems and control. Prentice Hall, Englewood Cliffs, USA, p 07458Google Scholar
  17. 17.
    Yu W, Li X (2004) Fuzzy identification using fuzzy neural networks with stable learning algorithms. IEEE Trans Fuzzy Syst 12(3):411–420CrossRefGoogle Scholar
  18. 18.
    Yu W, Ferreyra A (2004) System identification with state-space recurrent fuzzy neural networks. 43rd IEEE Conference on Decision and Control. Bahamas, pp 5106–5111Google Scholar
  19. 19.
    Yu W, Ferreyra A (2005) On-line clustering for nonlinear system identification using fuzzy neural networks. IEEE International Conference on Fuzzy Systems, pp 678–683Google Scholar
  20. 20.
    Yu W, Rubio JJ, Li X (2005) Recurrent neural networks training with stable risk-sensitive Kalman Filter algorithm. International Joint Conference on Neural Networks. IJCNN’05, Montreal, Canada, pp 700–704Google Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  1. 1.Instituto Politécnico Nacional, ESIME Azcapotzalco, Sección de Estudios de Posgrado e InvestigaciónMéxico, D.F.Mexico

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