Analysis of Techniques for Anfis Rule-base Minimization and Accuracy Maximization

Despite of acquiring popularity among researchers, the implementations of ANFIS-based models face problems when the number of rules surge dramatically and increase the network complexity, which consequently adds computational cost. Essentially, not all the rules in ANFIS knowledge-base are the potential ones. They contain those rules which have either minor or no contribution to overall decision. Thus, removing such rules will not only reduce complexity of the network, but also cut computational cost. Thus, there are various rule-base optimization techniques, proposed in literature, which are presented in motivation to simultaneously obtain rule-base minimization and accuracy maximization. This paper analyzes some of those approaches and important issues related to achieving both the contradictory objectives simultaneously. In this paper, Hyperplane Clustering, Subtractive Clustering, and the approach based on selecting and pruning rules are analyzed in terms of optimizing ANFIS rule-base. The optimized rule-base is observed in connection with providing high accuracy. The results and analysis, presented in this paper, suggest that the clustering approaches are proficient in minimizing ANFIS rule-base with maximum accuracy. Although, other approaches, like putting threshold on rules’ firing strength, can also be improved using metaheuristic algorithms.