Principles of adaptive filters and self-learning systems
Author(s)
Bibliographic Information
Principles of adaptive filters and self-learning systems
（Advanced textbooks in control and signal processing）
Springer, 2005
- : pbk
Available at 8 libraries
Note
Includes bibliographical references and index
Description and Table of Contents
Description
Teaches students about classical and nonclassical adaptive systems within one pair of covers
Helps tutors with time-saving course plans, ready-made practical assignments and examination guidance
The recently developed "practical sub-space adaptive filter" allows the reader to combine any set of classical and/or non-classical adaptive systems to form a powerful technology for solving complex nonlinear problems
Table of Contents
Part I: Introduction Adaptive Filtering Linear Systems and Stochastic Processes Part II: Modelling Optimisation and Least Square Estimation Parametric Signal and System Modelling Part III: Classical Filters and Spectral Analysis Optimum Wiener Filter Optimal Kalman Filter Power Spectral Density Analysis Part IV: Adaptive Filter Theory Adaptive Finite Impulse Response Filters Frequency Domain Adaptive Filters Adaptive Volterra Filters Adaptive Control Systems Part V: Nonclassical Adaptive Systems Introduction to Neural Networks Introduction to Fuzzy Logic Systems Introduction to Genetic Algorithms Part VI: Adaptive Filter Application Applications of Adaptive Signal Processing Generic Adaptive Filter Structures
by "Nielsen BookData"