Error-Driven Varying-Gain in Zeroing Neural Networks for Solving Time-Varying Quadratic Programming Problems

Le résumé fourni par la source

Convergence is a critical performance metric when zeroing neural networks (ZNNs) are employed to solve time-varying problems. As an effective strategy to enhance the convergence of ZNN models, gain adjustment is widely adopted to accelerate convergence speed, with the core objective of enabling the residual error to converge to zero rapidly and accurately. However, existing gain-tuning methods are decoupled from the residual error. This decoupling may lead to oscillation when the residual error approaches zero. To address this limitation, this paper proposes a novel error-driven varying-gain scheme. In this scheme, the gain value dynamically adapts to the residual error; a large residual error triggers a large gain, while a small residual error corresponds to a small gain, ensuring the gain changes synchronously with the residual error. Theoretical analyses and experimental results collectively demonstrate that integrating this error-driven varying-gain into ZNNs yields superior convergence performance, providing a more reliable solution for time-varying problem solving with ZNNs.

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