Evolving stable behavior in a spino-neuromuscular system model | Zendy

Stan Gotshall | Zendy; Terry Soule | Zendy

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Evolving stable behavior in a spino-neuromuscular system model

Author(s) -

Stan Gotshall,

Terry Soule

Publication year - 2008

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.1145/1389095.1389140

Subject(s) - stability (learning theory) , control theory (sociology) , forearm , computer science , control (management) , control system , feature (linguistics) , artificial intelligence , simulation , psychology , engineering , machine learning , anatomy , biology , electrical engineering , linguistics , philosophy

This paper demonstrates the effectiveness of genetic algorithms in training stable behavior in a model of the spino-neuromuscular system (SNMS). In particular, we test the stability of trained instances of the model with respect to unfamiliar control signals and untrained forearm weights. The results show that small changes to the input frequency and forearm weight result in small changes in velocity, demonstrating that the system can reasonably accommodate unfamiliar circumstances. This type of stability is a critical feature for virtually any type of control system.

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