Advanced Digital Signal Processing Methods for Filtering, Identification, and Nonlinear Systems Control

This book presents a general approach to block and recursive filtering, identification, and control, using signal observations processing techniques, and provides to the reader these results:

The new version of least square algorithm that is speeded up without changing its adaptive characteristics, increasing the parallelism in algorithm.

The efficient lower triangular inverse matrix and the input signal covariance matrix computation method.

The original bias correction approach that is used to eliminate the parameter estimation bias of an iterative autoregressive system parameter estimation algorithm in the presence of additive white noise.

The discovery that nonlinear Volterra, polynomial autoregressive and bilinear systems have the same layered implementation routine, which allows us using the layered structure, the order of nonlinearity increased/decreased by adding/deleting more layers to/from the structure.

The proven statement that the modular layered structures admit the very large scale integration implementation of the polynomial nonlinear filters.

The methods and algorithms for parametrical identification of linear time-invariant systems having piecewise-linear nonlinearities consisting of two blocks (linear dynamic block and a nonlinear static one) by partitioning different segments of the linear nonlinearity.

The simple mathematical equations representing self-tuning controllers for speedy servo control of nonlinear systems with piecewise-linear nonlinearities;

The approach to be proposed here could be used not only for a potential system having the invertible piecewise nonlinearity; it is applicable for a system having noninvertible nonlinear function as well.

The technique for adaptive control of nonlinear systems, containing switched piecewise-linear nonlinearities and operating in a noisy environment.

The simulation results of control of block-oriented Wiener and Hammerstein systems, using input-noisy output observations.

The book is aimed at three major groups of readers: senior undergraduate students, graduate students, and scientific research workers in electrical engineering, computer engineering, computer science, and digital control.