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Adaptive Neuro Fuzzy Inference System (ANFIS) with Error Backpropagation Algorithm using Mapping Function

Endra Joelianto, Basuki Rahmat


Adaptive Neuro Fuzzy Inference System (ANFIS) is a class of adaptive networks which enjoys many of the advantages claimed by neural networks (NNs) and the linguistic interpretability of Fuzzy Inference Systems (FIS). The fixed membership functions used in the backward pass lead to a problem in minimizing discrepancy between the actual outputs and the desired outputs. To overcome this problem, we propose a method to modify ANFIS algorithm in the backward pass by using a mapping function. The function maps the inputs to all corrected values obtained via error correction rules in the first layer by means of an interpolation of the inputs and the corrected values in the first layer. Simulation results demonstrate the effectiveness of the proposed modified ANFIS in significantly reducing the discrepancy between the actual outputs and the desired output.


Adaptive neuro fuzzy inference system; fuzzy systems; neural networks; backpropagation algorithm; mapping function

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