Neural network architecture for solving the algebraic matrix Riccati equation

Fredric M. Ham; Emmanuel G. Collins

doi:10.1117/12.235921

22 March 1996 Neural network architecture for solving the algebraic matrix Riccati equation

Fredric M. Ham, Emmanuel G. Collins

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235921
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

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.

Citation Download Citation

Fredric M. Ham and Emmanuel G. Collins "Neural network architecture for solving the algebraic matrix Riccati equation", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235921

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