Feedforward neural networks (FNN) are most heavily used to identify the relation between a given set of input and desired output patterns. By the universal approximation theorem, it is clear that a single-hidden layer FNN is suffcient for the outputs to approximate the corresponding desired outputs arbitrarily close and so we consider a single-hidden layer FNN. In practice, we set up an error function so as to measure the performance of the FNN. As the error function is nonlinear, we define an iterative process, learning algorithm, to obtain the optimal choice of the connection weights and thus set up a numerical optimization problem. In this paper, we consider a new error function defined on the hidden layer We propose a new learning algorithm based on the least square methods converges rapidly. We discuss our method with the classic learning algorithms and the convergence for these algorithms.
New Error Function for Single Hidden Layer Feedforward Neural Networks
2008 Congress on Image and Signal Processing ; 5 ; 752-755
01.05.2008
194129 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Predicting Telephone Traffic Congestion Using Multi Layer Feedforward Neural Networks
Trans Tech Publications | 2011
|Two Order Training Algorithm for Multi-layer Feedforward Neural Networks
British Library Online Contents | 1997
|Hidden-layer Redundancy Method of RBF Neural Networks
British Library Online Contents | 2001
|