Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/50
Título: Genetic programming and bacterial algorithm for neural networks and fuzzy systems design
Autor: Cabrita, Cristiano Lourenço
Botzheim, J.
Ruano, A. E.
Kóczy, László T.
Palavras-chave: Controlo automático
Redes neuronais
Sistemas fuzzy
Programação genética
Algoritmo bacteriano
Constructive algorithms
Genetic programming
Bacterial evolutionary algorithm
Fuzzy rule base
Data: 2003
Editora: Faro
Citação: IFAC International Conference on Intelligent control Systems and Signal Processing (ICONS). - Faro, 8-11 Abril 2003. - 6 p
Resumo: 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.
URI: http://hdl.handle.net/10400.1/50
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