Fuzzy systems are able to treat uncertain and imprecise information; t
hey make use of knowledge in the form of linguistic rules. Their drawb
acks are caused mainly by the difficulty of defining accurate membersh
ip functions and lack of a systematic procedure for the transformation
of expert knowledge into the rule base. Neural networks have the abil
ity to learn but with some neural networks, knowledge representation a
nd extraction are difficult. First, we consider a rule based fuzzy con
troller and a learning procedure based on the stochastic approximation
method. The Radial Basis Function neural network is then considered a
nd it is shown that a modified form of this network is identical with
the fuzzy controller, which may thus be considered as a neuro-fuzzy co
ntroller. Numerical examples are presented to demonstrate the validity
of the approach and it is shown that such an adaptive neuro-fuzzy sys
tem is successful in the control of a mobile robot.