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A Self-Evolving Interval Type-2 Fuzzy Neural Network and Its Hardware Implementation
|關鍵字:||第二類型模糊;Type-2 fuzzy systems;類神經網路;evolving system;structure learning;on-line fuzzy clustering;fuzzy neural networks||出版社:||電機工程學系所||引用:|| N. N. Karnik , J. M. Mendel, and Q. Liang, “Type-2 fuzzy logic systems,” IEEE Trans. on Fuzzy Systems, vol. 7, no. 6, pp. 643-658, 1999.  J. M. Mendel and R. I. John, “Type-2 fuzzy sets made simple,” IEEE Trans. On Fuzzy Systems, vol. 10, no. 2, pp. 117-127, 2002.  J. M. Mendel, Uncertain Rule-Based Fuzzy Logic System: Introduction and New Directions, Prentice Hall, Upper Saddle River, NJ2001.  Q. Liang and J. M. Mendel, “Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters,” IEEE Trans. Fuzzy systems, vol. 8, no. 551-563, 2000.  H. B. Mitchell, “Pattern recognition using type-2 fuzzy sets,” Information Sciences, vol. 170, pp. 409-418, 2005.  P. Melin and O. Castillo, “Intelligent control of non-linear dynamic plants using type-2 fuzzy logic and neural networks,” Proc. IEEE Int. Conf. Fuzzy Systems, Budapest, Hungary, July, 2004.  H. Hagras, “A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots,” IEEE Trans. Fuzzy Systems, vol. 12, no. 524-539, 2004.  M. Melgarejo and C. Pena-Reyes, “Hardware architecture and FPGA implementation of a type-2 fuzzy system,” Proc. Of Great Lakes Symposium on VLSI (GLSVLSI), Boston, USA, pp. 458-261, 2004.  R. I. John, P. R. Innocent, and M. R. Barnes, “Neuro-fuzzy clustering of radiographic tibia image data using type-2 fuzzy sets,” Information Sciences, vol. 125, pp. 203-220, 2000.  A. G. Luigi Di Lascio and A. Nappi, “Medical differential diagnosis through type-2 fuzzy sets,” Proc. Of IEEE Int. Conf. Fuzzy Systems, pp. 371-376, 2005.  J. S. Jang, “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, pp. 665-685, May 1993.  C. J. Lin and C. T. Lin, “An ART-based fuzzy adaptive learning control network,” IEEE Trans. Fuzzy Systems, vol. 5, no. 4, pp. 477-496, Nov. 1997.  C. F. Juang and C. T. Lin, “An on-line self-constructing neural fuzzy inference network and its applications,” IEEE Trans. Fuzzy Systems, vol. 6. no. 1, pp. 12-32, Feb. 1998.  D. Kukolj and E. Levi, “Identification of complex systems based on neural and Takagi-Sugeno fuzzy model,” IEEE Trans. Sys., Man, Cybern., Part B: Cybernetics, vol. 34, no. 1, pp. 272-282, 2004.  C. S. Ouyang; W. J. Lee and S. J. Lee, “A TSK-type neurofuzzy network approach to system modeling problems,” IEEE Trans. Sys., Man, Cybern., Part B: Cybernetics, vol. 35, no. 4, pp. 751- 767, 2005.  Q. Liang and J. M. Mendel, “Interval type-2 fuzzy logic systems: theory and design,” IEEE Trans. On Fuzzy Systems, vol. 8, no. 5, pp. 535-550, 2000.  C. H. Lee, Y. C. Lin, and W. Y. Lai, “Systems identification using type-2 fuzzy neural network (Type-2 FNN) systems,” Proc. IEEE Int. Symp. Computational Intelligence in Robotics and Automation, vol. 3, pp. 1264-1269, 2003.  C. H. Wang, C. S. Cheng, and T. T. Lee, “Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN),” IEEE Trans. on Syst., , Man, and Cyber. - Part B: Cybernetics, vol. 34, no. 3, pp. 1462-1477, 2004.  J. M. Mendel, “Computing derivatives in interval type-2 fuzzy logic system,” IEEE Trans. On Fuzzy Systems, vol. 12. no. 1, pp. 84-98, Feb. 2004.  H. Hagras, “Comments on dynamical optimal training for interval type-2 fuzzy neural network (T2FNN),” IEEE Trans. Syst., Man and Cyber. - Part B: Cybernetics, vol. 36, no. 5, pp. 1206-1209, Oct. 2006.  G. M. Mendez and O. Castillo, “Interval type-2 TSK fuzzy logic systems using hybrid learning algorithm,” Proc. IEEE Int. Conf. Fuzzy Systems, pp. 230-235, May 22-25, 2005.  O. Castillo and P. Melin, “Adaptive noise cancellation using type-2 fuzzy logic and neural networks,” Prof. IEEE Int. Conf. Fuzzy Systems, vol. 2, pp. 1093-1098, July 2004.  C.T. Lin and C.F. Juang, “An adaptive neural fuzzy filter and its applications,” IEEE Trans. Syst., Man, Cybern.  D. Kim and C. Kim, “Forecasting time series with genetic fuzzy predictor ensemble,” IEEE Trans. Fuzzy Systems, vol. 5, no. 4, pp. 523-535, 1997.  M. Russo, “Genetic fuzzy learning,” IEEE Trans. Evolutionary Computation, vol. 4, pp. 259-273, 2000.||摘要:||
This paper proposes a Self-Evolving Interval Type-2 Fuzzy Neural Network (SEIT2FNN) with on-line structure and parameter learning. The antecedent parts in each fuzzy rule of SEIT2FNN are interval type-2 fuzzy sets and the fuzzy rules can be of Mamdani or Takagi-Sugeno-Kang (TSK) type. The initial rule-base in SEIT2FNN is empty and an on-line clustering method is proposed to generate fuzzy rules which flexibly partition the input space. In addition, to avoid the generation of highly overlapped fuzzy sets in each input variable, an efficient fuzzy set reduction method is proposed to determine whether a corresponding fuzzy set should be generated independently in each input variable when a new fuzzy rule is generated. For parameter learning, the consequent part parameters are tuned by rule-ordered Kalman filter algorithm for high accuracy learning performance. Detailed learning equations on applying rule-ordered Kalman filter algorithm to SEIT2FNN consequent part learning with rules being on-line generated are derived. The antecedent part parameters are learned by gradient descent algorithms. SEIT2FNN has been applied to simulations on nonlinear plant modeling, chaotic signal prediction, and adaptive noise cancellation. Comparisons with other type-1 and type-2 fuzzy systems in these examples have verified performance of SEIT2FNN.
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