Karlik B.Tokhi M.O.Alci M.2019-10-272019-10-2720021474-66701474-6670https://hdl.handle.net/11454/2313215th World Congress of the International Federation of Automatic Control, 2002 -- 21 July 2002 through 26 July 2002 -- 153189This paper presents an investigation into classifying myoelectric signals using a new fuzzy clustering neural network architecture for control of multifunction prostheses. Moreover, a comparative study of the classification accuracy of myoelectric signals using multi-layer perceptron with back-propagation algorithm, and the new fuzzy clustering neural network (FCNN) is presented. The myoelectric signals considered are used to classify four upper-limb movements, which are elbow flexion, elbow extension, wrist pronation and wrist supination, grasp, and resting. The results suggest that FCNN can generalise better than the multi-layer perceptron without requiring extra computational effort. The proposed neural network algorithm allows the user to learn better and faster. Copyright © 2002 IFAC.eninfo:eu-repo/semantics/closedAccessFuzzy clusteringMyoelectric signalNeural networkPattern recognitionConference Object351447451N/A