CTRNN Parameter Learning using Differential Evolution
Published in Frontiers in artificial intelligence and applications • Jan 1, 2008
Authors:,,
Ivanoe De Falco
Antonio Della Cioppa
Francesco Donnarumma
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 ...
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