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CTRNN Parameter Learning using Differential Evolution
Ivanoe De Falco, Antonio Della Cioppa, Francesco Donnarumma, Domenico Maisto, Roberto Prevete, Ernesto Tarantino
783 - 784
10.3233/978-1-58603-891-5-783
Frontiers in Artificial Intelligence and Applications
Volume 178: ECAI 2008
Abstract

Target behaviours can be achieved by finding suitable parameters for Continuous Time Recurrent Neural Networks (CTRNNs) used as agent control systems. Differential Evolution (DE) has been deployed to search parameter space of CTRNNs and overcome granularity, boundedness and blocking limitations. In this paper we provide initial support for DE in the context of two sample learning problems.

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