Stability analysis based on a control adjuster for switched neural networks by trajectory similarity.

This paper focuses on a class of exponential stability control problem for the stochastic neural networks (SNNs) with time‐varying delays and Markov jump (TVDMJ). As a prerequisite to the main theorem, the existence and uniqueness of the solution for the main system are proved via contraction map principle. For the stability control of the system, we design a controller Ct,xtδδ,m(t)$$ C\left(t,x\left(\left[\frac{t}{\delta}\right]\delta \right),m(t)\right) $$ which can be adjusted the parameters δ$$ \delta $$ to make the system stabilization. We use this controller to actively control the stability behavior of the system, rather than the papers by setting the appropriate parameters to make the system achieve a stable state. Based on the intermittent control, we design a novel trajectory similarity process control and obtain exponential stability results, which are less conservative than the existing theoretical results. Two examples based on some numerical calculation and simulation diagrams are presented to validate the effectiveness for the utilized techniques. [ABSTRACT FROM AUTHOR]