Extended dissipativity synchronization for Markovian jump recurrent neural networks via memory sampled-data control and its application to circuit theory

Document Type : Research Paper


1 Department of Mathematics, Sri Sarada College for Women (Autonomous), Salem 636016, India

2 Department of Mathematics, Faculty of Science and Technology, Phuket Rajabhat University, Phuket-83000, Thailand

3 Research Institute of Natural Science, Hanyang University, Seoul 04763, Korea

4 Intelligence Laboratory, Toyota Technological Institute, Nagoya, 468-8511, Japan


 The problem of synchronization with extended dissipativity for Markovian Jump Recurrent Neural Networks (MJRNNs) is investigated. For MJRNNs, a new memory sampled-data extended dissipative control approach is suggested here. Some sufficient conditions in terms of Linear Matrix Inequalities (LMIs) are acquired by suitably establishing a relevant Lyapunov - Krasovskii functional (LKF), wherein the master and the slave system of MJRNNs are quadratically stable. At last, a numerical section is provided, along with one of the applications in circuit theory that clearly illustrates the efficacy of the proposed method's performance.