Improved results on an extended dissipative analysis of neural networks with additive time-varying delays using auxiliary function-based integral inequalities

The issue of extended dissipative analysis for neural networks (NNs) with additive time-varying delays (ATVDs) is examined in this research. Some less conservative sufficient conditions are obtained to ensure the NNs are asymptotically stable and extended dissipative by building the agumented Lyapunov-Krasovskii functional, which is achieved by utilizing some mathematical techniques with improved integral inequalities like auxiliary function-based integral inequalities (gives a tighter upper bound). The present study aims to solve the H00, L2 - L00, passivity and (Q, S, R)-& gamma;-dissipativity performance in a unified framework based on the extended dissipativity concept. Following this, the condition for the solvability of the designed NNs with ATVDs is presented in the form of linear matrix inequalities. Finally, the practicality and effectiveness of this approach were demonstrated through four numerical examples.

Automated summary

This research investigates the issue of extended dissipative analysis for neural networks with additive time-varying delays. By constructing the augmented Lyapunov-Krasovskii functional and utilizing improved integral inequalities, such as auxiliary function-based integral inequalities, less conservative sufficient conditions are obtained to ensure the asymptotic stability and extended dissipativity of the neural networks. The study aims to solve the H_∞, L_2 - L_∞, passivity, and (Q, S, R)-γ-dissipativity performance problems in a unified framework based on the concept of extended dissipativity. The solvability condition of the designed neural networks with additive time-varying delays is presented in the form of linear matrix inequalities. Finally, the practicality and effectiveness of this approach are demonstrated through four numerical examples.