Keywords
Nonlinear dynamics
Dynamic bifurcation
Hopfield neural network
Three nonidentical delays
Fractional-order neural networks
Adams-Bashforth-Moulton method
How to Cite
Abstract
This study extends a traditional neural network model to an asymmetric Hopfield model incorporating triple time delays through a combined qualitative and quantitative analytical approach. The system is first linearized using Taylor series expansion, followed by the determination of bifurcation points in the resulting system of equations containing quadratic transcendental terms. An improved Adams-Bashforth-Moulton predictor-corrector method is employed for discretization analysis. Theoretical findings are validated through three numerical case studies, with further examination of how time delays influence the system’s dynamic behavior.
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