Input Maping and Simulation Analysis using Adaptive Network Based Fuzzy Inference System
Nisha Rajan S1, Akash Rajan2, Binulal B. R3
1Nisha Rajan S,  Asst. Prof. Department of Electronics & Communication Engineering,  Adoor, India.
2Akash Rajan, Research Scholar, Department of Mechanical Engineering,  Trivandrum, TVM, India.
3Binulal B. R,  Assoc. Prof. Department of Mechanical Engineering,  Adoor, India.
Manuscript received on January 04, 2015. | Revised Manuscript received on February 12, 2015. | Manuscript published on February 28, 2015. PP: 169-174  | Volume-4 Issue-3, February 2015. | Retrieval Number:  C3804024315/2013©BEIESP

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Abstract: Fuzzy logic control systems are structured numerical estimators. They combine both the numerical process and human like reasoning. Neural networks are numerical trainable dynamical systems that are able to emulate human brain functions; their connectionist structure can be used to find the proper parameters and structures that resemble human thinking rules for fuzzy logic controllers. Generally fuzzy logic is best applied to non linear, time varying, ill- defined systems, which are too complex for conventional control systems to apply. In this paper a new combinational connectionist structure is proposed which exploits the advantages of both the fuzzy and neural networks avoiding the rule-matching time of the inference engine in the traditional fuzzy logic system. Some examples are presented using MATLAB simulation to illustrate the performance and applicability of the proposed connectionist model.
Keywords: Fuzzifier, Membership function, Receptive field, Hybrid learning, Adaptivity, Input-output mapping, ANFIS, Training, Epoch.