The present work deals with an improved back-propagation algorithm based on Gauss-Newton numerical optimization method for fast convergence. The steepest descent method is used for the back-propagation. The algorithm is tested using various datasets and compared with the steepest descent back-propagation algorithm. In the system, optimization is carried out using multilayer neural network. The ...

The supervised learning of the discriminative convolutional neural network (ConvNet or CNN) is powered by back-propagation on the parameters. In this paper, we show that the unsupervised learning of a popular top-down generative ConvNet model with latent continuous factors can be accomplished by a learning algorithm that consists of alternatively performing back-propagation on both the latent f...

The paper demonstrates performance enhancement using selective cloning on evolutionary neural network over the conventional genetic algorithm and neural back propagation algorithm for data classification. Introduction of selective cloning improves the convergence rate of the genetic algorithm without compromising on the classification errors. The selective cloning is tested on five data sets. T...

—Regarding to the problems of low rate of convergence and fault saturation for neural network classifier based on the algorithm of error back propagation during the signal recognition, bee colony algorithm is applied in this paper so as to extract combined feature module of signal and suggest three different algorithms including algorithm with rapidly support, super self-adaption error back pr...

This paper presents the performance comparison between back propagation, recursive prediction error (RPE) and modified recursive prediction error (MRPE) algorithms for training multilayered perceptron networks. Back propagation is a steepest descent type algorithm that normally has slow convergence rate and the search for the global minimum often becomes trapped at poor local minima. RPE and MR...

For many classiication tasks, the set of available task instances can be roughly divided into regular instances and exceptions. We investigate three learning algorithms that apply a diierent method of learning with respect to regularities and exceptions, viz. (i) back-propagation, (ii) cascade back-propagation (a constructive version of back-propagation), and (iii) information-gain tree (an ind...