Work

Stability Analysis of Bidirectional Associative Memory Neural Networks with Time-Varying Delays via Second-Order Reciprocally Convex Approach

Year: 2025

Type: article

Abstract: This research examines the Lyapunov-based criterion for global asymptotic stability of Bidirectional Associative Memory (BAM) neural networks that have mixed-interval time-varying delays. Using a second-order reciprocally convex approach, this paper introduces a novel stability criterion for BAM neural networks with time delays. The literature has recently incorporated a few triple integral expressions in the Lyapunov–Krasovskii functional to lessen conservatism in the analysis of system stability with interval time-varying delays using a second-order reciprocally convex combination strategy. This research work establishes the negative definiteness of the Lyapunov–Krasovskii functional derivative and is formulated using Linear Matrix Inequalities (LMIs). The effectiveness of the proposed result is demonstrated through numerical examples.

Source: Symmetry

DOI: 10.3390/sym17111852

Authors: Kalaivani Chandran, Renuga Kuppusamy, Vembarasan Vaitheeswaran

Institutions: Sri Sivasubramaniya Nadar College of Engineering, St. Joseph's Institute of Technology, Shiv Nadar University, Chennai

Language: English

Topic: Neural Networks Stability and Synchronization

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

OpenAlex ID: W4415813917

FWCI:

Cites: 35

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