内容简介
本书为国家科学技术学术著作出版基金项目，着眼于新一代人工智能应用系统中的前沿基础理论突破，主要研究Markov切换随机系统的稳定性分析及控制，来源于作者的研究工作及相关成果。本书主要针对不同类型的随机系统，从指数稳定性与控制理论两方面进行研究，能创新性确定动态系统的指数稳定性以及估计其指数收敛速度。随机系统的稳定性基本上取决于其预期应用。指数稳定性特性保证了无论发生任何的转换，网络快速存储活动模式的能力都不会因自组织而改变；率先就理论价值和实践价值上回应当前国内外研究与应用的热点课题，推动人工智能的发展。本书的海外版由Springer出版社出版。
目录
1 Introduction
1.1 Stochastic Systems with Markovian Switching Parameters
1.2 Stability Analysis
1.3 Controllability
1.4 Preview of This Book
1.5 Some Useful Definitions and Lemmas
1.6 Abbreviations and Notations
2 Exponential Stability of Neural Networks with Markovian Switching Parameters and General Noise
2.1 Introduction
2.2 Problem Formulation and Preliminaries
2.3 Main Results
2.4 Numerical Examples
2.5 Conclusion
3 Exponential Stability for Markovian Neutral Stochastic Systems with General Transition Probabilities and Time-Varying Delay
3.1 Introduction
3.2 Problem Formulation and Preliminaries
3.3 Main Results
3.4 Numerical Examples
3.5 Conclusion
4 A Traverse Algorithm Approach to Stochastic Stability Analysis of Markovian Jump Systems with Unknown and Uncertain Transition Rates
4.1 Introduction
4.2 Problem Statement and Preliminaries
4.3 Numerical Examples
4.4 Conclusion
5 Finite-Time Filtering for It? Stochastic Markovian Jump Systems with Distributed Time-Varying Delays
5.1 Introduction
5.2 Problem Statement and Preliminaries
5.3 Numerical Examples
5.4 Conclusion
6 A Distributed Dynamic Event-Triggered Mechanism to HMM-Based Observer Design for Sliding Mode Control of Markov Jump Systems
6.1 Introduction
6.2 Problem Preliminaries
6.3 Main Results
6.4 Numerical Examples
6.5 Conclusion
7 Structure-Triggered Asymptotical Synchronization for Nonidentical Generalized Stochastic Systems with Markovian Jumping Parameter Based on Sliding Mode Control
7.1 Introduction
7.2 Preliminary
7.3 Main Results
7.4 Numerical Examples
7.5 Conclusion
References
