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- Genetic programming and bacterial algorithm for neural networks and fuzzy systems designPublication . Cabrita, Cristiano Lourenço; Botzheim, J.; Ruano, Antonio; Kóczy, László T.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.
- Genetic programming and bacterial algorithm for neural networks and fuzzy systems designPublication . Cabrita, Cristiano Lourenço; Botzheim, J.; Ruano, Antonio; Kóczy, László T.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.
- Fuzzy rule base extraction by the improved bacterial memetic algorithmPublication . Gál, László; Botzheim, J.; Kóczy, László T.; Ruano, AntonioIn this paper we introduce new methods for handling knot order violation occurred in the Bacterial Memetic Algorithm (BMA) used for fuzzy rule base extraction. These methods perform slightly better than the method used before and are easier to integrate with the Bacterial Memetic Algorithm.
- Bacterial algorithm applied for fuzzy rule extractionPublication . Botzheim, J.; Hámori, B.; Kóczy, László T.; Ruano, AntonioThis paper presents a method of using the so-colled "bacterial algorithm" (4,5) for extracting a fuzzy rule base from a training set. The bewly proposed bacterial evolutionary algorithm (BEA) is shown. In our application one bacterium corresponds to a fuzzy rule system.
- 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.