This unified survey of the theory of Adaptive filtering, prediction, and Control focuses on linear discrete-time systems and explores the natural extensions to nonlinear systems.
Readers will find that these theories, formulas, and applications are related to a variety of fields, including biotechnology, aerospace engineering, computer sciences, and electrical engineering..
Extensive appendices offer a summary of relevant background material, making this volume largely self-contained.
The second part examines stochastic systems, exploring optimal Filtering and prediction, parameter estimation, Adaptive Filtering and prediction, and Adaptive control.
The first section concerns deterministic systems, covering models, parameter estimation, and Adaptive Prediction and control.
Ideal for advanced undergraduate and graduate classes, this treatment consists of two parts.
Their approach summarizes the theoretical and practical aspects of a large class of Adaptive algorithms.
In keeping with the importance of computers to practical applications, the authors emphasize discrete-time systems.
This unified survey of the theory of Adaptive filtering, prediction, and Control focuses on linear discrete-time systems and explores the natural extensions to nonlinear systems
