Browsing by Author "Botzheim, J."
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- An hybrid training method for B-spline neural networksPublication . Cabrita, Cristiano Lourenço; Botzheim, J.; Ruano, Antonio; Kóczy, László T.Current and past research has brought up new views related to the optimization of neural networks. For a fixed structure, second order methods are seen as the most promising. From previous works we have shown how second order methods are of easy applicability to a neural network. Namely, we have proved how the Levenberg-Marquard possesses not only better convergence but how it can assure the convergence to a local minima. However, as any gradient-based method, the results obtained depend on the startup point. In this work, a reformulated Evolutionary algorithm - the Bacterial Programming for Levenberg-Marquardt is proposed, as an heuristic which can be used to determine the most suitable starting points, therefore achieving, in most cases, the global optimum.
- Applying bacterial memetic algorithm for training feedforward and fuzzy flip-flop based neural networksPublication . Gál, László; Botzheim, J.; Kóczy, László T.; Ruano, AntonioIn our previous work we proposed some extensions of the Levenberg-Marquardt algorithm; the Bacterial Memetic Algorithm and the Bacterial Memetic Algorithm with Modified Operator Execution Order for fuzzy rule base extraction from inputoutput data. Furthermore, we have investigated fuzzy flip-flop based feedforward neural networks. In this paper we introduce the adaptation of the Bacterial Memetic Algorithm with Modified Operator Execution Order for training feedforward and fuzzy flipflop based neural networks. We found that training these types of neural networks with the adaptation of the method we had used to train fuzzy rule bases had advantages over the conventional earlier methods.
- 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.
- Design of B-spline neural networks using a bacterial programming approachPublication . Cabrita, Cristiano Lourenço; Botzheim, J.; Ruano, Antonio; Kóczy, László T.The design phase of B-spline neural networks represents a very high computational task. For this purpose, heuristics have been developed, but have been shown to be dependent on the initial conditions employed. In this paper a new technique, Bacterial Programming, is proposed, whose principles are based on the replication of the microbial evolution phenomenon. The performance of this approach is illustrated and compared with existing alternatives.
- Estimating fuzzy membership functions parameters by the levenberg-marquardt algorithmPublication . Botzheim, J.; Cabrita, Cristiano Lourenço; Kóczy, László T.; Ruano, AntonioIn previous papers from the authors fuzzy model identification methods were discussed. The bacterial algorithm for extracting fuzzy rule base from a training set was presented. The Levenberg-Marquardt algorithm was also proposed for determining membership functions in fuzzy systems. In this paper the Levenberg-Marquardt technique is improved to optimise the membership functions in the fuzzy rules without Ruspini-partition. The class of membership functions investigated is the trapezoidal one as it is general enough and widely used. The method can be easily extended to arbitrary piecewise linear functions as well.
- Estimating fuzzy membership functions parameters by the Levenberg-Marquardt algorithmPublication . Botzheim, J.; Cabrita, Cristiano Lourenço; Kóczy, László T.; Ruano, AntonioIn previous papers from the authors fuzzy model identification methods were discussed. The bacterial algorithm for extracting fuzzy rule base from a training set was presented. The Levenberg-Marquardt algorithm was also proposed for determining membership functions in fuzzy systems. In this paper the Levenberg-Marquardt technique is improved to optimise the membership functions in the fuzzy rules without Ruspini-partition. The class of membership functions investigated is the trapezoidal one as it is general enough and widely used. The method can be easily extended to arbitrary piecewise linear functions as well.
- Extension of the Levenberg-Marquardt algorithm for the extraction of trapezoidal and general piecewise linear fuzzy rulesPublication . Botzheim, J.; Kóczy, László T.; Ruano, AntonioThis paper discusses how training algorithms for determining membership functions in fuzzy rule based systems can be applied. There are several training algorithms, wbicb have been developed initially for neural networks mnd can be adapted to fumy systems. In this paper the Levenberg-Marquardt algorithm is introduced, allowing the determination of an optimal rukbase and converging faster tban some more classic methods (e.g. the standard Back Propagation algorithm). The class of membership funetions investigated is the trapezoidal one as it is general enough and widely used. The method can be easily extended to arbitrary piecewise linear function as well.
- Fuzzy model identification by evolutionary, gradient based and memtic algorithmsPublication . Botzheim, J.; Kóczy, László T.; Ruano, AntonioOne of the crucial problems of fuzzy rule modeling is how to find an optimal or at least a quasi-optimal rule base fro a certain system. In most applications there is no human expert available, or, the result of a human expert's decision is too much subjective and is not reproducible, thus some automatic method to determine the fuzzy rule base must be deployed.
- 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.