State Estimation for Discrete-Time Neural Networks with Randomly Occurring Quantisations

摘要：

　　This paper deals with the state estimation problem for a class of discrete-time neural networks with randomly occurring quantisations.The randomly occurring quantisation phenomenon is taken into account,which is governed by a Bernoulli distributed stochastic sequence.The purpose of the addressed problem is to design a state estimator such that the dynamics of the estimation error is exponentially stable in the mean square.By using the Lyapunov stability theory combined with the stochastic analysis techniques,sufficient conditions are first established to ensure the existence of the desired estimator.Then,the explicit expression of the desired estimator gain is described by using the semi-definite programme method.Finally,a numerical example is employed to demonstrate the effectiveness and applicability of the proposed estimator design approach.

关键词： Discrete-time neural networks State estimation Randomly occurring quantisations Lyapunov stability theory

作者: Jie Zhang Zidong Wang Derui Ding Yuming Bo

作者单位: School of Automation,Nanjing University of Science & Technology,Nanjing 210094,China Department of Computer Science,Brunel University,Uxbridge,Middlesex,UB8 3PH,United Kingdom School of Information Science and Technology,Donghua University,Shanghai 200051,China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 5403-5408

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）