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- Single and multi-objective genetic programming design for B-spline neural networks and neuro-fuzzy systemsPublication . Cabrita, Cristiano Lourenço; Ruano, Antonio; Fonseca, C. M.The design phase of B-spline neural networks and neuro-fuzzy systems is a highly computationally complex task. Existent heuristics, namely the ASMOD algorithm, have been found to be highly dependent on the initial conditions employed. A Genetic Programming approach is proposed, which produces an efficient topology search, obtaining additionally more consistent solutions. The facility to incorporate a multi-objective approach to the GP algorithm is exploited, enabling the designer to obtain better conditioned models, and more adequate for their intended use.
- Bacterial memetic algorithm for fuzzy rule base optimizationPublication . Cabrita, Cristiano Lourenço; Botzheim, J.; Gedeon, Tamas D.; Ruano, Antonio; Kóczy, László T.; Fonseca, C. M.In our previous works model identification methods were discussed. The bacterial evolutionary algorithm for extracting a fuzzy rule base from a training set was presented. The Levenberg-Marquardt method was also proposed for determining membership functions in fuzzy systems. The combination of evolutionary and gradient-based learning techniques – the bacterial memetic algorithm – was also introduced. In this paper an improvement of the bacterial memetic algorithm is shown for fuzzy rule extraction. The new method can optimize not only the rules, but can also find the optimal size of the rule base.
- Training neuro-fuzzy models using evolution based algorithmsPublication . Cabrita, Cristiano Lourenço; Ruano, Antonio; Fonseca, C. M.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.