Stability Analysis Of Neutral Type Neural Networks With Time Delay

As everyone knows, that make up the human nervous system the most basic is neurons, also known as neural cells. With the development of research on the neural network system and developing, its function is more and more powerful, capable of handling more and more problems. In the human nervous system, its all properties also constitute its different neural cell combination to achieve, although each neuron has its unique shape, but they are very similar. Based on the biological neural network knowledge learning, contribute more to learn and understand the structure and work principle of artificial neurons. This paper studies the neural network system stability problem of nonlinear system, the stability of nonlinear system control theory and promotes the research, has a certain theoretical value and practical value.Global asymptotic stability of neutral neural networks is researched. By choosing Lyapunov functional, based on the Lyapunov stability theory, by using the linear matrix inequality (LMI) and matrix inequality technique, be neutral delay neural network systems delay-dependent global asymptotic stability criterion.Exponential stability of neutral neural network is researched. Through the matrix inequality techniques have uniqueness criterion of equilibrium points of the system, and based on the Lyapunov stability theory, by using the linear matrix inequality (LMI) technology, get neutral delay neural networks with delay related index of new stability criterion.Global asymptotic stability of distribution delay neutral neural network is studied. By choosing Lyapunov functional, get neutral delay neural network stability criterion, the criterion is given in terms of linear matrix inequalities, are available through the Matlab LMI toolbox to solve, through simulation experiment, verified the validity and feasibility of the criterion.