Linear Hopfield Networks and Constrained Optimization
Published In
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Document Type
Citation
Publication Date
12-1-1999
Abstract
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.
Locate the Document
https://doi.org/10.1109/3477.740171
DOI
10.1109/3477.740171
Persistent Identifier
https://archives.pdx.edu/ds/psu/37313
Citation Details
Lendaris, G. G., Mathia, K., & Saeks, R. (1999). Linear Hopfield networks and constrained optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 29(1), 114-118.