alexa Linear Hopfield networks and constrained optimization
Engineering

Engineering

Journal of Information Technology & Software Engineering

Author(s): GG Lendaris

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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, whereas in the latter case it is used to implement an on-line solution to the inverse kinematics problem.

This article was published in IEEE Systems, Man, and Cybernetics Society and referenced in Journal of Information Technology & Software Engineering

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