Optimisation of ANFIS using mine blast algorithm for predicting strength of Malaysian small medium enterprises

Adaptive neuro-fuzzy inference system (ANFIS) is popular among other fuzzy inference systems, as it is widely applied in business and economics. Many have trained ANFIS parameters using metaheuristic algorithms, but very few have tried optimising its rule-base. The auto-generated rules, using grid partitioning, comprise both the potential and the weak rules which increase the complexity of ANFIS architecture as well as adding computational cost. Therefore, pruning weak rules will optimise the rule-base. However, reducing complexity and increasing accuracy of ANFIS network needs an effective training and optimisation mechanism. This paper proposes an efficient technique for optimising ANFIS rule-base without compromising on accuracy. A newly developed mine blast algorithm (MBA) is used to optimise ANFIS. The ANFIS optimised by MBA is employed to predict strength of Malaysian small and medium enterprises (SMEs). Results prove that MBA optimised ANFIS rule-base and trained parameters more efficiently than genetic algorithm (GA) and particle swarm optimisation (PSO).