C. Cabrita

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Publications - 3

Bacterial memetic algorithm for fuzzy rule base optimization

C. Cabrita J. Botzheim T. D. Gedeon Carlos Fonseca A. E. Ruano L. T. Koczy

Publication Name: 2006 World Automation Congress Wac 06

Publication Date: 2006-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

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 LevenbergMarquardt 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. Copyright - World Automation Congress (WAC) 2006.

Open Access: Yes

DOI: 10.1109/WAC.2006.376057

An hybrid training method for B-spline neural networks

C. Cabrita J. Botzheim A. E. Ruano L. T. Koczy

Publication Name: 2005 IEEE International Workshop on Intelligent Signal Processing Proceedings

Publication Date: 2005-12-01

Volume: Unknown

Issue: Unknown

Page Range: 165-170

Description:

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. © 2005 IEEE.

Open Access: Yes

DOI: DOI not available

Design of B-spline neural networks using a bacterial programming approach

C. Cabrita J. Botzheim A. E. Ruano L. T. Koczy

Publication Name: IEEE International Conference on Neural Networks Conference Proceedings

Publication Date: 2004-12-01

Volume: 3

Issue: Unknown

Page Range: 2313-2318

Description:

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.

Open Access: Yes

DOI: 10.1109/IJCNN.2004.1380987