Use of dynamic tunneling with backpropagation in training feedforward neural networks

An algorithm is proposed for supervised training in multilayer feedforward neural networks. The proposed algorithm consists of a gradient descent technique with a dynamic tunneling method for global optimization. Relative to the gradient descent technique like the backpropagation algorithm, the proposed technique gives a faster convergence, and the convergence properties of the proposed algorithm is demonstrated through three problems.