Stability Analysis And State Estimation For Several Classes Of Discrete-time Delayed Stochastic Memristive Neural Networks Subject To Network-induced Incomplete Information

In this thesis,we discuss the stability and state estimation issues for several discrete-time delayed stochastic memristive neural networks(DSMNNs)subject to networked incomplete information.The state-dependent parameter,time-delays,s-tochastic factors and their causes and variety laws are investigated deeply.Then the kinetic model with system model is established that is capable of representing time-delays and stochastic natures of the systems under consideration.After that,the global exponential stability issue is discussed for a class of memristive neural net-works(MNNs).Furthermore,the state estimation issue is investigated for a class of discrete-time stochastic memristive bidirectional associative memory neural network-s with mixed time-delays.Finally,considering missing measurements,event-trigged scheme and fading channels,we investigate the H? state estimation issue for several established DSMNNs.The descriptions of the thesis are given as follows:In Chapter 1,the research background and motivation are discussed,the outline and contribution of the thesis are introduced,and the research problems to be addressed in each individual chapters are also outlined.In Chapter 2,we investigate the globally exponential stability problem for a class of DSMNNs with both leakage delays as well as probabilistic time-varying delays.For the probabilistic delays,a sequence of Bernoulli distributed ran-dom variables is utilized to determine within which intervals the time-varying delays fall at certain time instant.The sector-bounded activation function is considered in the addressed DSMNN.By taking into account the state-dependent characteristics of the network parameters and choosing an appropri-ate Lyapunov-Krasovskii functional,some sufficient conditions are established under which the underlying DSMNN is globally exponentially stable in the mean square.In Chapter 3,the robust H? state estimation problem is investigated for a class of memristive recurrent neural networks with stochastic time-delays.The stochastic time-delays under consideration are governed by a Bernoulli dis-tributed stochastic sequence.we design the robust state estimator such that the dynamics of the estimation error is exponentially stable in the mean square,and the prescribed H? performance constraint is met.In Chapter 4,we study the H? state estimation problem for a class of discrete-time stochastic memristive bidirectional associative memory neural networks with mixed time delays.A series of novel switching functions are proposed to reflect the state-dependent characteristics of the memristive connection weight-s in the discrete-time setting,which facilitates the dynamics analysis of the addressed MNNs.By means of the introduced series of switching functions,an H? state estimator is designed such that the estimation error is exponen-tially mean-square stable and the prescribed H? performance requirement is achieved.In Chapter 5,the event-triggered H? state estimation problem is investigated for a class of DSMNNs with time-varying delays and missing measurements.The DSMNN is subject to both the additive deterministic disturbances and the multiplicative stochastic noises.The missing measurements are governed by a sequence of random variables obeying the Bernoulli distribution.For the purpose of energy saving,an event-triggered communication scheme is used for DSMNNs to determine whether the measurement output is transmitted to the estimator or not.By utilizing a Lyapunov-Krasovskii functional and stochastic analysis techniques,sufficient conditions are derived to guarantee the existence of the desired estimator.In Chapter 6,the H? state estimation issue is investigated for a sort of mem-ristive neural networks in the discrete-time setting under randomly occurring mixed time-delays and fading measurements.We put forward certain switching functions to account for the discrete-time yet state-dependent characteristics of the memristive connection weights.By resorting to the robust analysis theory and the Lyapunov-functional-like analysis theory,we derive some sufficient con-ditions to guarantee the desired estimation performance.The derived sufficient conditions rely not only on the size of discrete time-delays and the probabil-ity distribution law of the distributed time-delays,but also on the statistics information of the coefficients of the adopted Rice fading model.? In Chapter 7,we summarize the results of the thesis and discuss some future work to be further investigated.