Exploring the finite-time dissipativity of Markovian jump delayed neural networks

In this paper, we study the finite-time dissipativity analysis of Markovian jump-delayed neural networks (MJDNNs). The goal is to establish less conservative results for extended dissipativity conditions for delayed MJDNNs. To achieve this, an appropriate Lyapunov-Krasovskii functional (LKF) with novel inequality like composite slack-matrix-based integral inequality (CSMBII). Next, the CSMBII and other sufficient conditions are employed to estimate the derivative of the constructed LKF. Using these techniques, a delay-dependent finite-time dissipativity condition is derived in terms of linear matrix inequalities (LMIs). These LMIs are used to formulate the finite dissipativity condition for the delayed MJNNs. The utility of the suggested approach is then confirmed by a number of interesting numerical examples, one of which has been confirmed by a real-world application of the benchmark problem that is associated with the designed MJDNNs. The illustrative simulation results conclusively demonstrate the superior performance and success of the developed CSMBII technique in this proposal, surpassing the limitations of existing techniques.

Automated summary

In this paper, the finite-time dissipativity analysis of Markovian jump-delayed neural networks (MJDNNs) is studied. Less conservative results for extended dissipativity conditions are established for delayed MJDNNs. An appropriate Lyapunov-Krasovskii functional (LKF) with novel inequality, the composite slack-matrix-based integral inequality (CSMBII), is used to achieve this. Sufficient conditions including CSMBII are employed to derive a delay-dependent finite-time dissipativity condition in terms of linear matrix inequalities (LMIs), which are used to formulate the finite dissipativity condition for the delayed MJNNs. Numerical examples confirm the utility of the suggested approach, including a real-world application of the benchmark problem associated with the designed MJDNNs.