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A Simplified and Parsimonious Type-2 Fuzzy Neural Network with Two-Stage Learning and FPGA Implementation
|關鍵字:||Fuzzy chip;模糊晶片;fuzzy neural networks(FNNs);structure learning;type-2 fuzzy systems;模糊類神經網路;架構學習;第二型模糊系統||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. 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This paper proposes a Simplified and Parsimonious Type-2 Fuzzy Neural Network with two-stage learning (SPT2FNN). The antecedent part in each fuzzy rule of SPT2FNN uses interval type-2 fuzzy sets and the consequent part is Takagi-Sugeno-Kang (TSK) type. The SPT2FNN uses a simplified extended-output-calculation operation to reduce the computation time and hardware implementation cost. The initial rule set in the SPT2FNN is empty. The SPT2FNN uses a two-stage learning algorithm to construct interval type-2 fuzzy rules from extension of type-1 fuzzy rules. The objective of the first stage is to construct type-1 fuzzy rules via online structure learning and parameter learning. The second stage first extends the constructed type-1 fuzzy rules to interval type-2 fuzzy rules, where highly overlapped type-1 fuzzy sets are merged to interval type-2 fuzzy sets to reduce the total number of fuzzy sets. This stage then tunes consequent and antecedent parameters in the type-2 fuzzy rules using rule-ordered Kalman filter algorithm and gradient descent algorithm, respectively. SPT2FNN has been applied to simulations on system identification, stock price prediction, chaotic signal prediction, real-time series prediction and the robot arm mapping problems. Comparisons with several type-1 and type-2 fuzzy systems in these examples have verified the effectiveness and efficiency of SPT2FNN. A new hardware circuit is proposed to implement the learned SPT2FNN in an FPGA chip. The simplified function in the SPT2FNN helps to reduce hardware implementation cost.
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