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Proceedings Paper

Neural network architecture for solving the algebraic matrix Riccati equation
Author(s): Fredric M. Ham; Emmanuel G. Collins
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Paper Abstract

This paper presents a neurocomputing approach for solving the algebraic matrix Riccati equation. This approach is able to utilize a good initial condition to reduce the computation time in comparison to standard methods for solving the Riccati equation. The repeated solutions of closely related Riccati equations appears in homotopy algorithms to solve certain problems in fixed-architecture control. Hence, the new approach has the potential to significantly speed-up these algorithms. It also has potential applications in adaptive control. The structured neural network architecture is trained using error backpropagation based on a steepest-descent learning rule. An example is given which illustrates the advantage of utilizing a good initial condition (i.e., initial setting of the neural network synaptic weight matrix) in the structured neural network.

Paper Details

Date Published: 22 March 1996
PDF: 8 pages
Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235921
Show Author Affiliations
Fredric M. Ham, Florida Institute of Technology (United States)
Emmanuel G. Collins, Florida A&M Univ. and Florida State Univ. (United States)

Published in SPIE Proceedings Vol. 2760:
Applications and Science of Artificial Neural Networks II
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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