Traditionally, the autoregressivemoving average (ARMA)model has been one of themost widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with mixed conclusions in terms of the superior...

A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. One of the most successful methods to automate the fuzzy controllers development process are evolutionary algorithms. In this work, we propose a so-called “approximative” representation for fuzzy systems, where the antecedent of the rules are determined by a ...

The present work introduce a novel fuzzy approach to deal with the problem of parameter selection in the Evolutionay Computation EC Algorithms. In our approach a fuzzy clustering algorithm is used instead of a rule based system.The Fuzzy clustering gives freedom to the genetic algorithm to evolve simultaneously with the EC population.The crossover and mutation rates are optimized dynamically in...

| Coevolutionary algorithms have received increased attention in the past few years within the domain of evolutionary computation. In this paper, we combine the search power of coevolutionary computation with the expressive power of fuzzy systems, introducing a novel algorithm , Fuzzy CoCo: Fuzzy Cooperative Coevolution. We demonstrate the eecacy of Fuzzy CoCo by applying it to a hard, real-wor...

human language, over its evolutionary history, has emerged as one of the fundamental defining characteristic of the modern man. however, this milestone evolutionary process through natural selection has not left any ’linguistic fossils’ that may enable us to trace back the actual course of development of language and its establishment in human societies. lacking analytical tools to fathom the cr...

Fuzzy controllers are represented by if-then rules and thus can provide a user friendly and understandable knowledge representation. Evolutionary algorithms have been widely used for optimal design of fuzzy controllers. In this paper, we first present the state-of-art adaptive fuzzy control design using evolutionary algorithms and then attempt to enhance the performance of the simple genetic se...

Although fuzzy logic systems have been successfully applied in many complex industrial processes, they experience a deficiency in knowledge acquisition and rely to a great extent on empirical and heuristic knowledge, which, in many cases, cannot be objectively elicited. One of the most important considerations in designing fuzzy systems is construction of the membership functions for each fuzzy...

This study presents an evolutionary neural fuzzy system (NFS) for nonlinear system control. The proposed NFS model uses functional link neural networks (FLNNs) as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. A learning algorithm, which consists of structure lear...