Hybrid Fractional-neural Multi-agent Systems With Neutral Delay: Dynamical Stability and Predictive Ai Modeling
Year: 2026
Type: article
Abstract: This paper investigates the dynamical behavior of hybrid fractional-neural multi-agent systems with neutral delays modeled using Caputo fractional derivatives to capture nonlocal memory effects in neural dynamics. The proposed framework establishes positivity, boundedness, equilibrium existence, and stability using Lyapunov functionals, fractional Grönwall inequalities, and Mittag–Leffler stability theory. Positivity guarantees nonnegative solutions for biological and computational interpretations, while boundedness ensures finite state trajectories. Existence and uniqueness of equilibrium points are derived under Lipschitz conditions. A unified hybrid fractional-neural predictive architecture integrates fractional dynamic processing, deep neural feature extraction, time-delay embedding, and adaptive feedback control. The associated predictive control procedure is formalized in Algorithm where fractional operators, neural mappings, and delay interactions are fused through a coupled transformation to generate predictive outputs and adaptive control signals. Lyapunov-based adaptation laws ensure asymptotic convergence and closed-loop stability. Simulation results for fractional orders [Formula: see text], and demonstrate consistent convergence and accurate AI-based parameter prediction. The proposed methodology contributes to the stability theory of fractional-order systems and enhances predictive modeling capability for complex multi-agent neural networks.
Source: Fractals
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7165135642
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