内容简介：

    本书介绍基于TS方法的截断随机算法，并给出了新的收敛性证明。相较于已有的经典概率和ODE方法，该方法极大地弱化了保证收敛性所需要的条件，其中的收敛性定理在样本轨道上进行证明。该样本轨道表述下的定理易于应用，且同时考虑了量测噪声和结构噪声。本书对收敛速率、渐进正态性以及其它渐进性质做了讨论，同时也涉及了该方法运用于全局优化、盲轨道辨识、适应滤波、系统参数化辨识、适应稳定性等以及其它工程问题的讨论。

    本书适用于系统控制、优化、信号处理、通信及统计等领域内的本科和研究生及研究人员使用。

英文目录：
1  ROBBINS-MONRO ALGORITHM
   1.1 Finding Zeros of a Function.
   1.2 Probabilistic Method
   1.3 ODE Method
   1.4 Truncated RM Algorithm and TS Method
   1.5 Weak Convergence Method
   1.6 Notes and References
2  STOCHASTIC APPROXIMATION ALGORITHMS WITH EXPANDING TRUNCATIONS
   2.1 Motivation
   2.2 General Convergence Theorems by TS Method
   2.3 Convergence Under State-Independent Conditions
   2.4 Necessity of Noise Condition
   2.5 Non-Additive Noise
   2.6 Connection Between Trajectory Convergence and Property of Limit Points
   2.7 Robustness of Stochastic Approximation Algorithms
   2.8 Dynamic Stochastic Approximation
   2.9 Notes and References
3  ASYMPTOTIC PROPERTIES OF STOCHASTIC APPROXIMATION ALGORITHMS
   3.1 Convergence Rate: Nondegenerate Case
   3.2 Convergence Rate: Degenerate Case
   3.3 Asymptotic Normality
   3.4 Asymptotic Efficiency
   3.5 Notes and References
4  IMIZATION BY STOCHASTIC APPROXIMATION
   4.1 Kiefer-Wolfowitz Algorithm with Randomized Differences
   4.2 Asymptotic Properties of KW Algorithm
   4.3 Global Optimization
   4.4 Asymptotic Behavior of Global Optimization Algorithm
   4.5 Application to Model Reduction
   4.6 Notes and References
5  LICATION TO SIGNAL PROCESSING
   5.1 Recursive Blind Identification
   5.2 Principal Component Analysis
   5.3 Recursive Blind Identification by PCA
   5.4 Constrained Adaptive Filtering
   5.5 Adaptive Filtering by Sign Algorithms
   5.6 Asynchronous Stochastic Approximation
   5.7 Notes and References
6  APPLICATION TO SYSTEMS AND CONTROL
   6.1 Application to Identification and Adaptive Control
   6.2 Application to Adaptive Stabilization
   6.3 Application to Pole Assignment for Systems with Unknown Coefficients
   6.4 Application to Adaptive Regulation
   6.5 Notes and References
APPENDICES
   A.1 Probability Space
   A.2 Random Variable and Distribution Function
   A.3 Expectation
   A.4 Convergence Theorems and Inequalities
   A.5 Conditional Expectation
   A.6 Independence
   A.7 Ergodicity
   B.1 Convergence Theorems for Martingale
   B.2 Convergence Theorems for MDS I
   B.3 Borel-Cantelli-Lévy Lemma
   B.4 Convergence Criteria for Adapted Sequences
   B.5 Convergence Theorems for MDS II
   B.6 Weighted Sum of MDS