Training neuro-fuzzy models using evolution based algorithms

Cabrita, Cristiano Lourenço; Ruano, Antonio; Fonseca, C. M.

http://hdl.handle.net/10400.1/2328

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Authors

Cabrita, Cristiano Lourenço

Ruano, Antonio

Fonseca, C. M.

Abstract(s)

The normal design process for neural networks or fuzzy systems involve two different phases: the determination of the best topology, which can be seen as a system identification problem, and the determination of its parameters, which can be envisaged as a parameter estimation problem. This latter issue, the determination of the model parameters (linear weights and interior knots) is the simplest task and is usually solved using gradient or hybrid schemes. The former issue, the topology determination, is an extremely complex task, especially if dealing with real-world problems.

Keywords

Constructive algorithms B-splines Bacterial programming Genetic programming Memetic algorithms

URI

http://hdl.handle.net/10400.1/2328

Citation

Cabrita, C.; Ruano, A. E.; Fonseca, C. M. Training neuro-fuzzy models using evolution based algorithms, Trabalho apresentado em Global Education Techology Symposium (GETS 2006), In Proceedings of the Global Education Techology Symposium (GETS 2006), Faro, 2006.