Since the emergence of PID controllers, control system engineers are in pursuit of more and
more sophisticated versions of these controllers to achieve better performance, particularly …

We present a class of Learning Classifier Systems that learn fuzzy rule-based models,
instead of interval-based or Boolean models. We discuss some motivations to consider …

In this paper, a genetic algorithm (GA) based optimal fuzzy controller design is proposed.
The design procedure is accomplished by establishing an index function as the consequent …

This paper addresses the optimization and stabilization problems of nonlinear systems
subject to parameter uncertainties. The methodology is based on a fuzzy logic approach and …

We propose two evolutionary neural network-training algorithms for Beta basis function
neural networks (BBFNN). Classic training algorithms for neural networks start with a …

We propose an evolutionary method for the design of beta basis function neural networks
(BBFNN) and of beta fuzzy systems (BFS). Classical training algorithms start with a …

The task of fuzzy modelling involves specification of rule antecedents and determination of
their consequent counterparts. Rule premises appear here a critical issue since they …

This paper proposes a learning method for Beta fuzzy systems (BFS) based on a multiagent
genetic algorithm. This method, called Multi-Agent Genetic Algorithm for the Design of BFS …

A calibrated thermochromic liquid crystal technique was used to acquire wall temperature
data for laminar and turbulent forced convection in an asymmetrically heated channel. The …

We propose an evolutionary neural network-training algorithm for beta basis function neural
networks (BBFNN). Classic training algorithms for neural networks start with a …