Optimization of ANFIS using Mine Blast Algorithm for predicting strength of Malaysian small medium enterprises
Kashif Hussain, Mohd Najib Mohd Salleh, Abdul Mutalib Leman · 2015
Adaptive Neuro-Fuzzy Inference System (ANFIS) has been popular among other fuzzy inference systems. It has been widely applied in the field of business and economics. Many have trained ANFIS parameters using metaheuristic algorithms but very few have tried optimizing its fuzzy rule-base. The auto-generated rules, using grid partitioning, comprise of both the potential and weak rules. This increases the complexity of ANFIS architecture as well as the cost of computation. Therefore, pruning less or non-contributing rules would serve as optimizing ANFIS rule-base. However, reducing complexity and increasing accuracy of ANFIS network needs effective training and optimization mechanism. This paper proposes an efficient technique for optimizing ANFIS rule-base without compromising on accuracy. The proposed technique uses a newly developed optimization algorithm called Mine Blast Algorithm (MBA) for the first time for ANFIS learning. The ANFIS optimized by MBA is employed to model strength prediction for Malaysian small medium enterprises (SMEs). The results prove that MBA optimized ANFIS rule-base and trained its parameters more efficiently than Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).