AbstractThis paper presents the result of research in developing a novel training model for Adaptive Neuro-Fuzzy Inference Systems (ANFIS). ANFIS integrates the learning ability of Artificial Neural Networks with the Takagi-Sugeno Fuzzy Inference System to approximate nonlinear functions. Therefore, it is considered as a Universal Estimator. The original algorithm used in ANFIS training process has a hybrid model that uses Steepest Decent Derivative; therefore, it inherits low convergence rate and local minima during training. In this study, a training algorithm is proposed that combines Bees Algorithm (BA) and Least Square Estimation (LSE) (BA-LSE). The local and global exploration of BA as integrates with the best-fit solution of the LSE improves current shortcomings of ANFIS training process. The proposed training algorithm is examined under three different scenarios of function approximation, time series prediction, and classification experiments in order to verify the promising improvements in the tr...
['Hosein Marzi', 'Ahmed Haj Darwish', 'Humam Helfawi']
Training ANFIS using The Enhanced Bees Algorithm and Least Squares Estimation