基于可达集策略的交换中立型神经网络同步无颠簸传输控制

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Fang Li, Hong Sang, Peng Wang, Ying Zhao, Yajing Ma, Georgi M. Dimirovski
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引用次数: 0

摘要

本文主要研究了基于可达集的交换中性型神经网络(SNNNs)同步的无碰撞传输控制(BTC)。为了缓解传统状态依赖交换策略(sdss)和组合交换策略(CSS)固有的保守性,提出了一种利用神经元状态历史信息和中立延迟的改进的状态依赖交换策略。通过构造一个时间相关的多重Lyapunov-Krasovskii泛函(TDMLF)技术,首先建立了一个较保守的可达集估计准则(RSE)。随后,BTC进一步使用已建立的设计框架来实现snnn的同步。导出了相应的同步准则,保证了受有界外部输入影响的同步误差可以限制在预期的有界集合内。此外,在切换瞬间的潜在控制颠簸在切换瞬间有效地约束到一个特定的水平。最后,通过两个仿真实例验证了所提设计框架的实用性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bumpless Transfer Control for Synchronization of Switched Neutral-Type Neural Networks With a Reachable Set Strategy

This investigation primarily centers on the reachable-set-based bumpless transfer control (BTC) for the synchronization of switched neutral-type neural networks (SNNNs). In order to mitigate the conservatism inherent in the traditional state-dependent switching strategies (SDSSs) and combined switching strategies (CSSs), an improved CSS leveraging the historical information of neuron states and neutral delay is developed. By constructing a time-dependent multiple Lyapunov-Krasovskii functional (TDMLF) technique, a less conservative criterion for reachable set estimation (RSE) is first established. In the subsequent, the established design framework is further employed by the BTC for the synchronization of SNNNs. The corresponding synchronization criterion is derived, which ensures that the resultant synchronization error influenced by bounded external inputs can be confined to an anticipated bounded set. Also, the underlying control bumps at switching instants during switching instants are effectively constrained to a specific level. Ultimately, the practicability and superiority of the proposed design framework are confirmed via two simulation examples.

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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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