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This book begins with a brief background on the §field of active disturbance cancellation with a §particular focus on the challenges of acoustic §cancellation environments. Traditional linear §feedforward and feedback approaches are discussed §and §a novel method for 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.
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