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

Voting algorithms are extensively used to make decisions in fault tolerant systems where each redundant module gives inconsistent outputs. Popular voting algorithms include majority voting, weighted voting, and inexact majority voters. Each of these techniques suffers from scenarios where agreements do not exist for the given voter inputs. This has been successfully overcome in literature using fuzzy theory. Our previous work concentrated on a neuro-fuzzy algorithm where training using the neuro system substantially improved the prediction result of the voting system. Weight training of Neural Network is sub-optimal. This study proposes to optimize the weights of the Neural Network using Artificial Bee Colony algorithm. Experimental results show the proposed system improves the decision making of the voting algorithms. Keywords—Voting algorithms, Fault tolerance, Fault masking, Neuro-Fuzzy System (NFS), Artificial Bee Colony (ABC)