Exponential Stabilization for Delayed Memristive Neural Networks by Comparison Strategy

This article is concerned with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i>th moment global exponential stabilization of delayed memristive neural networks (DMNNs). By using the comparison strategy, the theories of differential inclusion and inequality techniques, the exponential stabilization of the DMNNs is investigated. To achieve this purpose, a state feedback controller and an adaptive controller are designed, respectively. The comparison strategy is a new analytical method without employing Lyapunov stability theory and relaxes the constraint of time delays. In addition, the obtained results are represented by algebraic criteria, which are convenient for testing. In the end, a numerical simulation is given to show the validity of the derived criteria.