Training the random neural network using quasi-Newton methods

Training in the random neural network (RNN) is generally speci®ed as the minimization of an appropriate error function with respect to the parameters of the network (weights corresponding to positive and negative connections). We propose here a technique for error minimization that is based on the use of quasi-Newton optimization techniques. Such techniques o€er more sophisticated exploitation of the gradient information compared to simple gradient descent methods, but are computationally more expensive and dicult to implement. In this work we specify the necessary details for the application of quasi-Newton methods to the training of the RNN, and provide comparative experimental results from the use of these methods to some well-known test problems, which con®rm the superiority of the approach. Ó 2000 Elsevier Science B.V. All rights reserved.