Research ArticleFurther improvement on delay-dependent robust stability criteria for neutral-type recurrent neural networks with time-varying delays

- This paper has presented improved delay-dependent robust stability criteria for neutral-type recurrent neural networks with time-varying delays.
- The developed robust stability criteria have delay dependence and the results are characterized by LMIs.
- Less conservative robust stability criteria have been developed based on the integral inequality approach.
- Five well-known examples show that these methods reduced conservatism and improved the maximal allowable delay.

This paper is concerned with the problem of improved delay-dependent robust stability criteria for neutral-type recurrent neural networks (NRNNs) with time-varying delays. Combining the Lyapunov-Krasovskii functional with linear matrix inequality (LMI) techniques and integral inequality approach (IIA), delay-dependent robust stability conditions for RNNs with time-varying delay, expressed in terms of quadratic forms of state and LMI, are derived. The proposed methods contain the least number of computed variables while maintaining the effectiveness of the robust stability conditions. Both theoretical and numerical comparisons have been provided to show the effectiveness and efficiency of the present method. Numerical examples are included to show that the proposed method is effective and can provide less conservative results.