A fuzzy curved search algorithm for neural network learning

In this paper, we develop a curved search algorithm which uses second-order information, for the learning algorithm for a supervised neural network. With the objective of reducing the training time, we introduce a fuzzy controller for adjusting the first and second-order approximation parameters in the iterative method to further reduce the training time and to avoid the spikes in the learning curve which sometimes occurred with the fixed step length. Computational results indicate a significant reduction in training when comparing with the delta learning rule.