Evolving fuzzy models for myoelectric-based control of a prosthetic hand

This paper proposes a set of evolving Takagi-Sugeno-Kang (TSK) fuzzy models of the nonlinear dynamic mechanisms occurring in the myoelectric-based control of prosthetic hand fingers. The rule bases and the parameters of the TSK fuzzy models are evolved by an online identification algorithm. The experimental results prove the performance of the TSK fuzzy models by good output responses and root mean square error values. A performance comparison with two recurrent neural network architectures is included.