A Neuro-Fuzzy Approach Based Voting Scheme for Fault Tolerant Systems Using Artificial Bee Colony Training

Voting algorithms make decisions in fault tolerant systems where a redundant module provides inconsistent outputs. Popular voting algorithms are majority voting, weighted voting and inexact majority voters. Each technique suffers from areas where agreements are lacking for voter inputs. This was overcome using fuzzy theory in literature. Our earlier work concentrated on a neuro-fuzzy algorithm where training using neuro system improved the voting system's prediction result. Neural Network Weight training is sub-optimal. This paper proposes to optimize the weights of the Neural Network using Hybrid Artificial Bee Colony algorithm. Experimental results show the proposed system improves the decision making of the voting algorithms.

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