The problem of stability analysis for the general class of random pulsed and switching neural networks is presented in this paper, which is to be investigated both continuous dynamics and impulsive jumps of random disturbances. Two numerical examples are used to explain and highlight the effectiveness of the results developed.The purpose of this article is to provide a comprehensive overview of studies, including continuous time and discrete time models for solving various problems, and their application in motion planning and superfluous manipulator management, chaotic system tracking, or even population control in mathematical biological sciences. Considering the fact that real-time performance is in demand for time-varying problems in practice, analysis of the stability and convergence of various models with continuous time is considered in a unified form in detail. In the case of solving the problems of discrete time, procedures are summarized for how to discriminate a continuous model and methods for obtaining an accuracy decision.
Due to its strong ability to extract features and autonomous learning, neural networks are rooted in many industries, for example. neuroscience, mathematics, informatics and engineering, transport, etc. Despite their widespread use in various fields, such as artificial intelligence, language recognition, and computer simulation, the issue of neural network stability analysis is the most primary and fundamental that has attracted intense attention in recent decades.and references therein.
It is well known that pulse and switching systems are formulated by combining pulse systems with switching systems, which is a more complex model of nonlinear systems. With their increasing use in network management, power systems, and the like, impulse control theory and switching systems have been a hot topic of research for the past decade. The fruitful results of research on stability analysis and control design of pulse and switching systemssuch as input stability, time-limited, controllability and observation and feedback control design, etc. On the other hand, it is also noteworthy.
Keywords: artificial neural network, electric transport, numerical algorithms, control reliability
Neural Network Inverse Model Control Strategy: Discrete-Time Stability Analysis for Relative Order Two Systems
