Optimization of Fuzzy Neural Networks using Mine Blast Algorithm for Classification Problem - Mohd Salleh, Mohd Najib; Hussain, Kashif (Transactions on Engineering and Computing Sciences, 2017)
The integration of Fuzzy Neural Networks (FNNs) with optimization techniques has not only solved the issues “black box” in Artificial Neural Networks (ANNs) but also has been effective in a wide variety of real-world applications. Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rules optimization methods to perform efficiently when the number of inputs increases. ANFIS accuracy depends on the parameters it is trained with and the drawbacks of gradients based learning of ANFIS using gradient descent and least square methods in two-pass learning algorithm. Many researchers have trained ANFIS parameters using metaheuristic, however, very few have considered optimizing the ANFIS rule-base. We propose an effective technique for optimizing ANFIS rule-base and training the network parameters using newly Accelerated modified MBA (AMBA) to convergence the speed during exploitation phase. The AMBA optimized ANFIS was tested on real-world benchmark classification problems like Breast Cancer, Iris, and Glass. The AMBA optimized ANFIS has also been employed to model real datasets. The performance of the proposed AMBA optimized ANFIS model was compared with the ones optimized by Genetic Algorithm (GA), Particle Swarm Optimization (PSO), MBA and Improved MBA (IMBA), respectively. The results show that the proposed AMBA optimized ANFIS achieved better accuracy with optimized rule-set in less number of function evaluations. Moreover, the results also indicate that AMBA converges earlier than its other counterparts.