Exponential stability of delayed bi-directional associative memory networks

• Published in: Applied mathematics and computation Vol. 135; no. 1; pp. 105 - 112
• Main Authors: Cao, Jinde, Dong, Meifang
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 15.02.2003; Elsevier
• Subjects: Bi-directional associative memory (BAM); Classical ensemble theory; Classical statistical mechanics; Delay; Exact sciences and technology; Global exponential stability; Inequality; Lyapunov functional; Mathematical analysis; Mathematics; Neural circuits; Neural network; Ordinary differential equations; Physics; Sciences and techniques of general use; Statistical physics, thermodynamics, and nonlinear dynamical systems; Global exponential stability; Lyapunov functional; Neural circuits; Neural network; Delay; Bi-directional associative memory (BAM); Inequality; Sufficient condition; Neural networks; Global stability; Neural circuit; Circuit; Delays; Exponential stability; Global solution; Numerical stability; Lyapunov function; Bidirectional associative memory
• ISSN: 0096-3003; 1873-5649
• DOI: 10.1016/S0096-3003(01)00315-0

Some sufficient conditions are derived for the global exponential stability in delayed bi-directional associative memory (BAM) networks by constructing suitable Lyapunov functional and the inequality 2 ab⩽ a 2+ b 2 technique. These conditions have an important leading significance in the design and applications of globally exponentially stable neural circuits for delayed BAM.