This paper investigates delay-dependent stability for continuous neural networks with a time-varying delay. This paper aims at deriving a new stability criterion, considering tradeoff between conservativeness and calculation complexity. A new Lyapunov–Krasovskii functional with simple augmented terms and delay-dependent terms is constructed, and its derivative is estimated by several techniques, including free-weighting matrix and inequality estimation methods. Then, the influence of the techniques used on the conservativeness and the complexity is analyzed one by one. Moreover, useful guidelines for improving criterion and future work are briefly discussed. Finally, the advantages of the proposed criterion compared with the existing ones are verified based on three numerical examples.

Stability Analysis for Delayed Neural Networks Considering Both Conservativeness and Complexity

Delay-dependent stability analysis of neural networks with time-varying delay

State estimation of T–S fuzzy delayed neural networks with Markovian jumping parameters using sampled-data control ☆

This paper focuses on the problem of delay-dependent stability and dissipativity analysis of generalized neural networks (GNNs) with Markovian jump parameters and two delay components. By constructing novel augmented Lyapunov–Krasovskii functional (LKF), using free-matrix-based inequality to estimate the derivative of Lyapunov function and employing the reciprocally convex approach to consider the relationship between the time-varying delay and its interval, some improved delay-dependent stability criteria and dissipativity criteria are established in terms of linear matrix inequalities. Moreover, the obtained criteria is extended to analyze the stability analysis of GNNs with two delay components and the passivity analysis of GNNs with one delay. Numerical examples are given to show the effectiveness and the significant improvement of the proposed methods.

Hongjiang Ma

Papers

Parameter adaptation-based ant colony optimization with dynamic hybrid mechanism

Improved delay-dependent stability criteria for recurrent neural networks with time-varying delays