Genetic programming and bacterial algorithm for neural networks and fuzzy systems design

• Autor(a) principal: Cabrita, Cristiano Lourenço
• Data de Publicação: 2003
• Outros Autores: Botzheim, J., Ruano, Antonio, Kóczy, László T.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.1/50
• topic: Controlo automático; Redes neuronais; Sistemas fuzzy; Programação genética; Algoritmo bacteriano; 681.5; Constructive algorithms; B-splines; Genetic programming; Bacterial evolutionary algorithm; Fuzzy rule base

In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use.