IMPROVED CONVERGENCE THROUGH DYNAMIC PROGRAMMING APPROACH
Angelica Calu · Cybernetics & Systems · 2002
New hybrid methods for solving the multiplayer perceptron optimization problem are proposed which use the computation capabilities of Bellman's dynamic programming (DP) method. To solve the neural network optimization problem, we consider the case of output neurons differently from that of hidden neurons. For the neurons of the output layer we apply the conventional DP and for the hidden neurons we apply a method based on gradient approach. Computer simulation shows that the new hybrid methods outperform the gradient-based optimization methods in converging speed and avoiding the local minimum.