具有混合延迟的随机记忆神经网络的自适应滑动模式固定/预分配时间同步化

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Processing Letters Pub Date : 2024-06-21 DOI:10.1007/s11063-024-11669-y
Jie Gao, Xiangyong Chen, Jianlong Qiu, Chunmei Wang, Tianyuan Jia
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引用次数: 0

摘要

本文探讨了具有不确定参数和混合延迟的随机记忆神经网络(MNN)的固定/预分配时间同步问题。主要采用了自适应滑模控制(ASMC)技术。首先,构建适当的滑动面并给出自适应规律。同时,设计了同步控制方案,确保误差系统实现定时稳定性。其次,主要提供了预分配时间滑模控制方案,以实现 MNN 的快速同步。所提出的理论方法可以保证误差系统在预分配时间内达到和滑动模式的收敛性和稳定性。此外,还获得了同步标准和沉降时间(ST)的明确表达式,其中 ST 与初始值和控制器参数无关,可以优先预定义。最后,本文提供了一个计算实例,以解释本文创新的实用性和可用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Adaptive Sliding Mode Fixed-/Preassigned-Time Synchronization of Stochastic Memristive Neural Networks with Mixed-Delays

The paper addresses the fixed-/preassigned-time synchronization of stochastic memristive neural networks (MNNs) with uncertain parameters and mixed delays. Adaptive sliding mode control (ASMC) technology is mainly utilized. First, a proper sliding surface is constructed and the adaptive laws are given. Also, the synchronization control scheme is designed, which can ensure error system to realize fixed-time stability. Second, preassigned-time sliding mode control scheme is mainly provided to realize fast synchronization of MNNs. The presented theoretical methods can guarantee the error system convergence and stability for reaching and sliding mode within preassigned-time. And the synchronization criteria and explicit expression of settling time (ST) are acquired, where ST is not related with initial values and controller parameters but can be predefined perferentially. Finally, the calculation example is offered to interpret the practicability and availability of the innovations in this paper.

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来源期刊
Neural Processing Letters
Neural Processing Letters 工程技术-计算机:人工智能
CiteScore
4.90
自引率
12.90%
发文量
392
审稿时长
2.8 months
期刊介绍: Neural Processing Letters is an international journal publishing research results and innovative ideas on all aspects of artificial neural networks. Coverage includes theoretical developments, biological models, new formal modes, learning, applications, software and hardware developments, and prospective researches. The journal promotes fast exchange of information in the community of neural network researchers and users. The resurgence of interest in the field of artificial neural networks since the beginning of the 1980s is coupled to tremendous research activity in specialized or multidisciplinary groups. Research, however, is not possible without good communication between people and the exchange of information, especially in a field covering such different areas; fast communication is also a key aspect, and this is the reason for Neural Processing Letters
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