The paper presents a method for times series prediction using a local dynamic modeling based on a three step process. In the first step the input data is embedded in a reconstruct...
We consider two layered binary state neural networks in which cellular topographic self-organization occurs under correlational learning. The main result is that for separable inpu...
- In this paper we investigate mixture of experts problems in the context of Local-Global Neural Networks. This type of architecture was originaly conceived for functional approxim...
We propose a self organizing map (SOM) for sequences by extending standard SOM by two features, the recursive update of Sperduti [7] and the hyperbolic neighborhood of Ritter [5]. ...
We propose an event-driven framework dedicated to the design and the simulation of networks of spiking neurons. It consists stract model of spiking neurons and an efficient event-d...
In this paper, we present an algorithm that minimizes the mutual information between the outputs of a perceptron with two hidden layers. The neural network is then used as separati...
This paper presents our approach to the rule extraction problem from trained neural network. A method called REX is briefly described. REX acquires a set of fuzzy rules using an ev...
Small recurrent neural network with two and three neurons are able to control autonomous robots showing obstacle avoidance and photo-tropic behaviors. They have been generated by e...
Abstract. The purpose of this study is to develop subject categorization methods for educational resources using multilayer perceptron (MLP) and to examine the performance of the t...