Adaptive Filter TheoryStochastic processes and models - Wiener filters - Linear prediction - Method of steepest descent - Least-mean-square adaptive filters - Normalized least-mean-square adaptive filters - Frequency-domain and subband adaptive filters - Method of least squares - Recursive least-squares adaptive filters - Kalman filters - Square-root adaptive filters - Order-recursive adaptive filters - Finite-precision effects - Tracking of time-varying systems - Adaptive filters using infinite-duration impulse response structures - Blind deconvolution - Back-propagation learning. |
Contents
Background and Preview | 1 |
A | 4 |
Approaches to the Development of Linear Adaptive Filters | 14 |
Copyright | |
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adaptive filter adaptive filtering algorithms applied array autocorrelation function backward prediction errors backward prediction-error filter beamformer bm(n channel Chapter complex computation convergence correlation matrix corresponding cost function cross-correlation denote derived described desired response d(n discrete-time stochastic process eigenvalue equal estimation error FIGURE filter of order filter output fm(n formulation forward prediction Gaussian impulse response input data input vector u(n inputs u(n inverse Jmin Kalman filter lattice predictor linear prediction LMS algorithm LMS filter minimum mean-square error multilayer perceptron MVDR nonlinear normalized LMS operation optimum orthogonal power spectral density problem process u(n QR-RLS algorithm recursive reflection coefficients result RLS filter samples Section sequence side of Eq squares stationary process statistical step-size parameter stochastic process subspace tap inputs tap-weight vector transfer function transversal filter update v₁(n variables variance weight vector white noise wide-sense stationary Wiener filter Wiener-Hopf equations zero mean Σ Σ