A computer algebra system can be used to enhance numerous courses in applied mathematics. Not only can they alleviate the computational difficulties encountered, but they can offer the advantage of visualizing solutions. The solution of standard problems found in the study of ordinary and...

Fast training of feed-forward neural networks became increasingly important as the neural network field moves toward maturity. This paper begins with a review of various criteria proposed for training feed-forward neural networks, which include the frequently used quadratic error criterion, the...

Flexible and dexterous manipulation of modern industrial applications requires the use of robots with extra (more than six) axes of motion (degrees of freedom). Thus the kinematic and control considerations of redundant robots are currently of increasing interest to both theorists and...

In this paper the basic principles and developments of an unsupervised learning algorithm, the self-organizing map (SOM) and a supervised learning algorithm, the learning vector quantization (LVQ) are explained. Some practical applications of the algorithms in data analysis, data visualization...

The paper presents an efficient two-phase approach to picture interpretation based on original connectionist techniques. During the first phase invariant representations of individual objects are obtained based on third-order image correlations and appropriate neural network classifiers are used...

For systems in thermal equilibrium or systems with a Gaussian equilibrium distribution the response to small perturbations can be computed via a fluctuation-dissipation theorem from the correlations. For general open systems, especially systems with a low-dimensional chaotic attractor, this...

This paper describes a parallel simulation of an electrical circuit using the Transmission Line Modelling (TLM) method. The TLM method is used to decouple the circuit into sub-circuits which are then simulated concurrently in a parallel processing system. This approach not only simplifies the...

A direct nonlinear adaptive state regulator is derived, based on dynamic neural networks, and it is successfully applied to control the speed of a nonlinearized dc motor. One interesting feature of the proposed control algorithm is that it covers the situation where the magnetic flux...