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The work presented in this paper deals with the problem of the navigation of a mobile robot either in unknown indoor environment or in a partially known one. A navigation method in an unknown environment based on the combination of elementary behaviors has been developed. Most of these behaviors are achieved by means of fuzzy inference systems. The proposed… (More)

SUMMARY Most of the motion controls of the mobile robots are based on the classical scheme planning-navigation-piloting. The navigation function the main part of which consists in obstacle avoidance, has to react with the shortest response time. The real time constraint hardly limits the complexity of sensor data processing. The described navigator is built… (More)

Approximation Theory plays a central part in modern statistical methods, in particular in Neural Network modeling. These models are able to approximate a large amount of metric data structures in their entire range of definition or at least piecewise. We survey most of the known results for networks of neurone-like units. The connections to classical… (More)

This paper addresses the problem of supervised learning in layered Neural Network with linear units and includes an analysis of the effect of noise on training algorithms. We survey most of the known results on linear networks. The connections to classical statistical ideas such as ordinary Least Squares are emphasized. Notations Small boldface letters are… (More)