Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/7361
標題: 自我演化的第二類型模糊類神經網路和其硬體實現
A Self-Evolving Interval Type-2 Fuzzy Neural Network and Its Hardware Implementation
作者: 曹育瑋
Tsao, Yu-Wei
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: [1] 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. [2] 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. [3] J. M. Mendel, Uncertain Rule-Based Fuzzy Logic System: Introduction and New Directions, Prentice Hall, Upper Saddle River, NJ2001. [4] 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. [5] H. B. Mitchell, “Pattern recognition using type-2 fuzzy sets,” Information Sciences, vol. 170, pp. 409-418, 2005. [6] 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. [7] H. Hagras, “A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots,” IEEE Trans. Fuzzy Systems, vol. 12, no. 524-539, 2004. [8] 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. [9] 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. [10] 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. [11] J. S. Jang, “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, pp. 665-685, May 1993. [12] 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. [13] 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. [14] 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. [15] 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. [16] 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. [17] 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. [18] 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. [19] 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. [20] 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. [21] 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. [22] 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. [23] C.T. Lin and C.F. Juang, “An adaptive neural fuzzy filter and its applications,” IEEE Trans. Syst., Man, Cybern. [24] 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. [25] M. Russo, “Genetic fuzzy learning,” IEEE Trans. Evolutionary Computation, vol. 4, pp. 259-273, 2000.
摘要: 
本論文提出一個自我演化的區間第二類型模糊類神經網路(SEIT2FNN),此網路並具有線上架構和參數學習。SEIT2FNN中的每一條模糊規則的前件部都是第二類型的模糊集合,而模糊規則又可以為Mamdani或Takagi-Sugeno-Kang(TSK)兩種形式。一開始SEIT2FNN的模糊規則庫是空的,而線上分群的方法被提出來產生模糊規則。另外,為了避免在每個輸入變數上產生高度重疊的模糊集合,因此一個有效降低模糊集合的方法被提出來降低上述情況的可能性。當產生一個新的模糊規則時,會去計算其是否要在每個輸入變數上獨立產生相對應的模糊集合。在參數學習中,利用卡門濾波器演算法去調整後件部的參數可具有較準確的學習效能,其中利用卡門濾波器演算法於本篇論文中有詳細的學習公式被推導。前件部的參數則利用梯度下降法去學習。SEIT2FNN被應用在模擬非線性系統的建立、渾沌訊號預測和雜訊消除器。在本篇論文的例子中會比較type-2模糊系統和其它type-1模糊系統來證明SEIT2FNN具有較好的效能。

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.
URI: http://hdl.handle.net/11455/7361
其他識別: U0005-0507200713145500
Appears in Collections:電機工程學系所

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