内容简介：　　　

　　Recursive Identification and Parameter Estimation describes a recursive approach to solving system identification and parameter estimation problems arising from diverse areas. Supplying rigorous theoretical analysis, it presents the material and proposed algorithms in a manner that makes it easy to understand—providing readers with the modeling and identification skills required for successful theoretical research and effective application.

　　The book begins by introducing the basic concepts of probability theory, including martingales, martingale difference sequences, Markov chains, mixing processes, and stationary processes. Next, it discusses the root-seeking problem for functions, starting with the classic RM algorithm, but with attention mainly paid to the stochastic approximation algorithms with expanding truncations (SAAWET) which serves as the basic tool for recursively solving the problems addressed in the book.

　　The book not only identifies the results of system identification and parameter estimation, but also demonstrates how to apply the proposed approaches for addressing problems in a range of areas, including:

　　• Identification of ARMAX systems without imposing restrictive conditions

　　• Identification of typical nonlinear systems

　　• Optimal adaptive tracking

　　• Consensus of multi-agents systems

　　• Principal component analysis

　　• Distributed randomized PageRank computation

　　This book recursively identifies autoregressive and moving average with exogenous input (ARMAX) and discusses the identification of non-linear systems. It concludes by addressing the problems arising from different areas that are solved by SAAWET. Demonstrating how to apply the proposed approaches to solve problems across a range of areas, the book is suitable for students, researchers, and engineers working in systems and control, signal processing, communication, and mathematical statistics.

英文目录：

Preface
Acknowledgments
About the Authors
1 Dependent Random Vectors
  1.1 Some Concepts of Probability Theory
  1.2 Independent Random Variables, Martingales, and Martingale Difference Sequences
  1.3 Markov Chains with State Space (R^m,R^m)
  1.4 Mixing Random Processes
  1.5 Stationary Processes
  1.6 Notes and References
2 Recursive Parameter Estimation
  2.1 Parameter Estimation as Root-Seeking for Functions
  2.2 Classical Stochastic Approximation Method: RM Algorithm
  2.3 Stochastic Approximation Algorithm with Expanding Truncations
  2.4 SAAWET with Nonadditive Noise
  2.5 Linear Regression Functions
  2.6 Convergence Rate of SAAWET
  2.7 Notes and References
3 Recursive Identification for ARMAX Systems
  3.1 LS and ELS for Linear Systems
  3.2 Estimation Errors of LS/ELS
  3.3 Hankel Matrices Associated with ARMA
  3.4 Coefficient Identification of ARMAX by SAAWET
  3.5 Order Estimation of ARMAX
  3.6 Multivariate Linear EIV Systems
  3.7 Notes and References Recursive
4 Identification for Nonlinear Systems
  4.1 Recursive Identification of Hammerstein Systems
  4.2 Recursive Identification of Wiener Systems
  4.3 Recursive Identification of Wiener-Hammerstein Systems
  4.4 Recursive Identification of EIV Hammerstein Systems
  4.5 Recursive Identification of EIV Wiener Systems
  4.6 Recursive Identification of Nonlinear ARX Systems
  4.7 Notes and References
5 Other Problems Reducible to Parameter Estimation
  5.1 Principal Component Analysis
  5.2 Consensus of Networked Agents
  5.3 Adaptive Regulation for Hammerstein and Wiener Systems
  5.4 Convergence of Distributed Randomized PageRank Algorithms
  5.5 Notes and References
Appendix A: Proof of Some Theorems in Chapter 1
Appendix B: Nonnegative Matrices
References
Index