Improved Summation Inequality Based State Estimation for Stochastic Semi-Markovian Jumping Discrete-Time Neural Networks with Mixed Delays and Quantization

The problem of estimator design for stochastic discrete-time semi-Markov jump neural networks (NNs) with both quantization and mixed time delays is addressed. The asymptotic stability criteria are acquired by setting up an appropriate Lyapunov functional using the summation inequalities in both single and double forms for the semi-Markov jump networks. Making use of Lyapunov functional technique, the explicit expressions for the gain are proposed. Eventually, two examples are exploited numerically to exemplify the usefulness of the new methodology.

Automated summary

This study addresses the problem of estimator design for stochastic discrete-time semi-Markov jump neural networks (NNs) with both quantization and mixed time delays. The asymptotic stability criteria are acquired by establishing an appropriate Lyapunov functional using summation inequalities in both single and double forms for the semi-Markov jump networks. By utilizing the Lyapunov functional technique, explicit expressions for the gain are proposed. Two examples are numerically exploited to demonstrate the usefulness of the new methodology.