Neural Network approaches to time series prediction are briefly discussed, and the need to specify an appropriately sized input window identified. Relevant theoretical results fro...
This paper describes a novel network model, which is able to control its growth on the basis of the approximation requests. Two classes of self-tuning neural models are considered...
A. Carlevarino, R. Martinotti, Giorgio Metta, Giul...
The timely and accurate detection of computer and network system intrusions has always been an elusive goal for system administrators and information security researchers. Existin...
In our recent work, a general method called the stable state analysis technique was developed to determine constraints that the weights in the Hopfield energy function must satisf...
: This paper describes a rather novel method for the supervised training of regression systems that can be an alternative to feedforward Artificial Neural Networks (ANNs) trained w...
Mark J. Embrechts, Dirk Devogelaere, Marcel Rijcka...
This paper presents biologically inspired neural controllers for generating motor patterns in a quadruped robot. Sets of arti cial neural networks are presented which provide 1 p...
In the eld of arti cial evolution creating methods to evolve neural networks is an important goal. But how to encode the structure and properties of the neural network in the geno...
It has already been shown how Artificial Neural Networks (ANNs) can be incorporated into probabilistic models. In this paper we review some of the approaches which have been prop...