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An Adaptive Network Based Recurrent Self-Evolving Interval Type-2 Fuzzy Controller for a Flexible Link Manipulator
This paper introduces a new neuro-fuzzy approach to tip position regulation of a flexible link manipulator. The proposed Adaptive Network based Recurrent Self-Evolving Interval Type-2 Fuzzy Controller (ANRSEIT2FC) incorporates type-2 fuzzy sets in a recurrent neural fuzzy controller in order to handle problems with uncertainties and to increase the noise resistance of a system. The antecedent parts in each recurrent fuzzy rule in the ANRSEIT2FC are interval type-2 fuzzy membership functions, and the consequent part is of the Takagi-Sugeno type. At first, the ANRSEIT2FC contains no rules; all rules are generated on-line by structure and parameter learning: The structure learning uses on-line type-2 fuzzy clustering. For the parameter learning, the consequent part parameters are updated by Kalman filter algorithm and the antecedent type-2 fuzzy sets are learned by extended Kalman filter and gradiant descent algorithm. The efficacy of the ANRSEIT2FC is evaluated by comparing it with adaptive neuro-fuzzy inference system controller.
Recurrent fuzzy neural networks, type-2 fuzzy systems, adaptive network based recurrent self-evolving interval type-2 fuzzy logic controller, flexible link manipulator.
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