Improving the back propagation learning speed with adaptive neuro-fuzzy technique

A neuro-fuzzy technique is presented to improve the standard back propagation learning speed. By adjusting both the learning rate and accelerator parameters based on the system error and change of the error direction, the convergent rate of the proposed technique is found to be superior to that yielded by the conventional approach. Simulation results are given to demonstrate the applicability and efficiency of the proposed method.