Linear Hopfield Networks and Constrained Optimization 1
نویسندگان
چکیده
It is shown that a Hopfield neural network (with linear transfer functions) augmented by an additional feedforward layer can be used to compute the Moore-Penrose Generalized Inverse of a matrix. The resultant augmented linear Hopfield network can be used to solve an arbitrary set of linear equations or, alternatively, to solve a constrained least squares optimization problem. Applications in signal processing and robotics are considered. In the former case the augmented linear Hopfield network is used to estimate the " structured noise " component of a signal and adjust the parameters of an appropriate filter on-line, and in the latter case it is used to implement an on-line solution to the inverse kinematics problem via a Jacobi algorithm.
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