Abstract
In this paper posture control of a human arm in the sagittal plane is investigated by means of model simulations. The arm is modeled by a nonlinear neuromusculoskeletal model with two degrees of freedom and six muscles. A multilayer perceptron network is used in this paper, and effectively adapted by Levenberg-Marquardt training algorithm. The duration of next movement is regulated according as current feedback states. Simulation Results indicate that this method can maintain two joints at different location in allowable bound. The control scheme provides novel insight into neural prosthesis control and robotic control.
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Liu, S., Wang, Y., Huang, J. (2006). Neural Network Based Posture Control of a Human Arm Model in the Sagittal Plane. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_116
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DOI: https://doi.org/10.1007/11760191_116
Publisher Name: Springer, Berlin, Heidelberg
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