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We have been working toward the construction of a forward model arm employing a neural network model with electromyographic signals as the control input. We succeeded in estimating 1) the joint torque during isometric contraction in a plane from the electromyographic signals. 2) the path of motion from the degree of joint acceleration as well as 3) the path of motion from joint torque and 4) the position in three-dimensional space. In this paper, we present a new human interface employing a model of an arm, robot control of an artificial hand, and the learning of motion capability. In addition, based on the fact that we can estimate the position of the arm in three-dimensional space, we discuss as an example of the human interface the results of generating motion from electromyographic signals in a hypothetical arm using a neural network model. [ABSTRACT FROM AUTHOR]

When a random noise is superposed on a non-Gaussian signal with an abruptly changing component, as in the ease of the image signal, it is impossible for the conventional linear filter to eliminate the noise effectively. This is one of the cases where the nonlinear filter should be applied. This paper proposes a nonlinear digital filter based on the layered neural network, including the optimization of the nonlinear function. The proposed filter can effectively eliminate the noise by utilizing the nonlinearity of the layered neural network and its learning function. Another point is that the performance can be improved by the piecewise-linear approximation of the nonlinear function and the automatic design of the optimal nonlinear function. The effectiveness of the proposed filter is demonstrated by eliminating the noise from the two-dimensional image signal. [ABSTRACT FROM AUTHOR]