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|標題:||A Type-2 Self-Organizing Neural Fuzzy System and Its FPGA Implementation||作者:||Juang, C.F.
|關鍵字:||Fuzzy chip;fuzzy neural networks (FNNs);structure learning;system;identification;type-2 fuzzy systems;logic controller;temperature control;network;design;architecture;chip||Project:||Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics||期刊/報告no：:||Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics, Volume 38, Issue 6, Page(s) 1537-1548.||摘要:||
This paper proposes a type-2 self-organizing neural fuzzy system (T2SONFS) and its hardware implementation. The antecedent parts in each T2SONFS fuzzy rule are interval type-2 fuzzy sets, and the consequent part is of Mamdani type. Using interval type-2 fuzzy sets in T2SONFS enables it to be more robust than type-1 fuzzy systems. T2SONFS learning consists of structure and parameter identification. For structure identification, An online clustering algorithm is proposed to generate rules automatically and flexibly distribute them in the input space. For parameter identification, a rule-ordered Kalman filter algorithm is proposed to tune the consequent-part parameters. The learned T2SONFS is hardware implemented, and implementation techniques are proposed to simplify the complex computation process of a type-2 fuzzy system. The T2SONFS is applied to nonlinear system identification and truck backing control problems with clean and noisy training data. Comparisons between type-1 and type-2 neural fuzzy systems verify the learning ability and robustness of the T2SONFS. The learned T2SONFS is hardware implemented in a field-programmable gate array chip to verify functionality of the designed circuits.
|Appears in Collections:||電機工程學系所|
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