A Tool for Evolving Artificial Neural Networks
نویسندگان
چکیده
1 Technological Educational Institute of Kalamata, Greece, e-mail: [email protected] 2 Dept. of Medicine, Democritus University of Thrace, Greece, e-mail: [email protected] 3 Dept. of Computer Engineering & Informatics, University of Patras, Greece, e-mail: [email protected] Abstract. A hybrid evolutionary algorithm that combines genetic programming philosophy, with localized Extended Kalman Filter (EKF) training method is presented here. This algorithm is used for the topological evolution and training of Multi-Layered Neural Networks. It is implemented as a visual software tool in C++ programming language. The proposed hybrid evolutionary algorithm is applied on two bio-signal modeling tasks: the Magneto Encephalogram (MEG) of epileptic patients and the Magneto Cardiogram (MCG) of normal subjects, exhibiting very satisfactory results.
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