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Reduced Interval Type-2 Neural Fuzzy System Using Weighted Bound Set Boundaries for Computation Speedup and FPGA Implementation
|關鍵字:||模糊類神經系統;Neural fuzzy systems;第二類型模糊系統;類型降低;可解釋的模糊系統;可區分的模糊集合;type-2 fuzzy systems;type reduction;interpretable fuzzy systems;distinguishable fuzzy sets||出版社:||電機工程學系所||引用:|| 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. 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.  N. K. Kasabov and Q. Song, “DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction,” IEEE Trans. on Fuzzy Systems, vol. 10, no. 2, pp. 144 -154, April 2002.  P.P. Angelov and D. P. Filev, “An approach to online identification of Takagi-Sugeno fuzzy models,” IEEE Trans. on Systems, Man and Cybernetics, Part B: Cybernetics, vol. 34, no. 1, pp. 484-498, 2004.  E. D. 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This thesis proposes a reduced interval type-2 neural fuzzy system using weighted bound-set boundaries (RIT2NFS-WB) for simplification of type-reduction operation. The objective of this simplification is to reduce system training time in software implementation and chip size in hardware implementation, especially when the number of rules is large. The antecedent part in the RIT2NFS-WB uses interval type-2 fuzzy sets (IT2FSs) and the consequent can be of Takagi-Sugeno-Kang (TSK) or Mamdani type. The RIT2NFS-WB is built through online structure and parameter learning. In addition to model accuracy, interpretability of the RIT2NFS-WB is improved via considering distributions of the IT2FSs in the input variables. A distinguishability-oriented cost function is used in parameter learning to improve semantics-based interpretability and highly-overlapped IT2FSs are merged to reduce the number of IT2FSs and improve complexity-based interpretability. The software-implemented TSK-type RIT2NFS-WB is hardware implemented on a field-programmable gate array (FPGA) chip. The chip is characterized with online learning ability for TSK-type consequent and weighting parameters update based on the gradient descent algorithm. To accelerate the chip execution speed, the chip utilizes not only the parallel execution properties of fuzzy rules and bound-set boundaries but also the pipeline technique. In particular, flexibility of the chip is considered so that no re-design of the circuits is required when the RIT2NFS-WB is applied to different problems. Characteristics of RIT2NFS-WB and its hardware implementations are verified through various examples and comparisons with various type-1 and interval type-2 fuzzy models
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