Date of Award
Winter 2003
Abstract
A proposal for the use of a time delay CMAC neural network for disturbance cancellation in nonlinear dynamical systems is presented. Appropriate modifications to the CMAC training algorithm are derived which allow convergent adaptation for a variety of secondary signal paths. Analytical bounds on the maximum learning gain are presented which guarantee convergence of the algorithm and provide insight into the necessary reduction in learning gain as a function of the system parameters. Effectiveness of the algorithm is evaluated through mathematical analysis, simulation studies, and experimental application of the technique on an acoustic duct laboratory model.
Document Type
Dissertation
First Advisor
L Gordon Kraft
Department or Program
Engineering: Electrical
Degree Name
Doctor of Philosophy
Recommended Citation
Canfield, John C., "Active disturbance cancellation in nonlinear dynamical systems using neural networks" (2003). Doctoral Dissertations. 188.
https://scholars.unh.edu/dissertation/188