Exponentially Dissipative Control And State Estimation For Neural Networks With Mixed Interval Time-vary Delays

Neural networks(NNs) have been used in variety of areas, such as signal process-ing, pattern recognition, static image processing, associative memory, and combinatorial optimization in the past decades. This paper focuses on the exponentially dissipative con-trol and exponentially passive control problems for neural network systems with mixed interval time-varying delays. The structure of the paper is arranged as follows:First of all, we introduce the developmental situation and significance of the theory of dissipative control and passive control, and an overview of neural network systems with mixed interval time-vary ing delays is introduced, then we illustrate the arrangement of the content and structure of the research. Secondly, we research on the exponentially dissipative control and exponentially passive control for neural network systems with mixed interval time-varying delays, a sufficient condition is derived in terms of linear matrix inequality(LMI), which can guarantee the neural network systems with mixed in-terval time-varying delays to be exponentially dissipative and exponentially passive, then state estimator is designed, which can guarantee error system to be asymptotically sta-ble. Thirdly, we research on the robust exponential dissipativity and robust exponential passivity control for uncertain neural network systems with mixed interval time-varying delays, a sufficient condition is derived in terms of linear matrix inequality (LMI), which can guarantee the uncertain neural network systems with mixed interval time-varying delays to be robust exponentially dissipative and robust exponentially passive for all ad-missible uncertainties, then state estimator is designed, which can guarantee error system to be robust asymptotically stable for all admissible uncertainties.then the feasibility of the theorems are verified by some numerical examples. Finally, the paper is summarized.