In this paper, real-time implementation of multirobot convoying behavior utilizing neuro-fuzzy control is presented. With only nine rules, the follower convoys the leader closely and smoothly by varying its speed and changing its direction. When the leader stops, the follower stops very close to the specified safe distance (within an inch). The follower backs up quickly when the leader moves ba...

This paper deals problem of intelligent hybrid systems. Intelligent systems include neural networks (NN), fuzzy systems (FS) and genetic algorithms (GA). Each of these intelligent systems has certain properties (ability of learning, modelling, classifying, obtaining empirical rules, solving optimizing tasks ...) fitting specific kind of applications. Combination of these intelligent systems cre...

We propose a novel approach for neuro-fuzzy system modeling. A neuro-fuzzy system for a given set of input-output data is obtained in two steps. First, the data set is partitioned automatically into a set of clusters based on input-similarity and output-similarity tests. Membership functions associated with each cluster are defined according to statistical means and variances of the data points...

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The composition of simple local models for approximating complex nonlinear mappings is a common practice in recent modeling and control literature. This paper presents a comparative analysis of two di,erent local approaches: the neuro-fuzzy inference system and the lazy learning approach. Neuro-fuzzy is a hybrid representation which combines the linguistic description typical of fuzzy inference...

An adaptive method to construct compact fuzzy systems for solving pattern classiication problems is presented. The method consists of two phases: a rule identiication phase and a rule selection phase. The rule identiication phase generates fuzzy rules from numerical data through a simple fuzzy grid method, then tunes the resulting fuzzy rules by training a neuro-fuzzy network used to model the ...