Adaptive Filter Theory"Adaptive Filter Theory" looks at both the mathematical theory behind various linear adaptive filters with finite-duration impulse response (FIR) and the elements of supervised neural networks. Up-to-date and in-depth treatment of adaptive filters develops concepts in a unified and accessible manner. This highly successful book provides comprehensive coverage of adaptive filters in a highly readable and understandable fashion. Includes an extensive use of illustrative examples; and MATLAB experiments, which illustrate the practical realities and intricacies of adaptive filters, the codes for which can be downloaded from the Web. Covers a wide range of topics including Stochastic Processes, Wiener Filters, and Kalman Filters. For those interested in learning about adaptive filters and the theories behind them. |
Contents
INTRODUCTION | 1 |
STATIONARY DISCRETETIME STOCHASTIC PROCESSES | 44 |
WIENER FILTER THEORY | 100 |
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Accordingly adaptive adaptive filter applied assume average backward Chapter coefficients complex compute condition Consider consists constant convergence correlation matrix corresponding defined definition denote derived described desired response determined deterministic difference eigenvectors elements equals equation estimation error experiment express factor Figure follows forward function given Hence independent initial inverse iterations Kalman lattice least-squares linear linear prediction LMS algorithm mean mean-squared error measurement method noise normal Note null vector observations obtained operation optimum orthogonal output parameter particular performance posteriori prediction error prediction-error filter predictor presented problem process u(n produced recursive refer relation represents respectively sample sequence shown side of Eq signal solution squares stationary structure substituting systolic array tap inputs tap-weight vector term theory transformation transversal filter triangular updated variable variance weight write zero