模糊惯性蜂窝神经网络的采样数据同步及其在安全通信中的应用

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

摘要

本文设计了采样数据控制(SDC)方案,以深入研究模糊惯性细胞神经网络(FICNN)的同步问题。从技术上讲,细胞神经元传输信息或激活的速率可以用一阶微分模型来描述,但网络对接收到的信息的响应可能与时间有关,可以用二阶(惯性)细胞神经网络(ICNN)模型来模拟。一般来说,模糊蜂窝神经网络(FCNN)是模糊逻辑和蜂窝神经网络的结合。模糊逻辑模型由输入和输出模板组成,这些模板采用乘积运算总和的形式,有助于在规则基础上评估信息传输。因此,本研究提出了一种用户控制的 FICNNs 模型,其动态特性与 FICNN 模型相同。在这方面,同步方法在确保驱动(无控制输入)和响应(有外部控制输入)的动态特性方面相当有效。从理论上讲,可以通过分析从驱动-响应得出的误差模型来确保驱动-响应之间的同步,但由于非线性因素,可以利用 Lyapunov 稳定性理论来推导线性矩阵不等式(LMI)的充分稳定条件,从而保证误差模型收敛到原点。与现有的稳定性条件不同的是,本文以二次函数的形式涉及延迟信息,并通过负确定性稃(NDL)评估下限和上限,从而推导出稳定性条件。此外,还讨论了支持验证所提理论框架的数值模拟。作为直接应用,FICNN 模型被视为图像加密和解密算法中的密码系统,并说明了相应的结果和安全措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sampled-data synchronization for fuzzy inertial cellular neural networks and its application in secure communication

This paper designs the sampled-data control (SDC) scheme to delve into the synchronization problem of fuzzy inertial cellular neural networks (FICNNs). Technically, the rate at which the information or activation of cellular neuronal transmission made can be described in a first-order differential model, but the network response concerning the received information may be dependent on time that can be modeled as a second-order (inertial) cellular neural network (ICNN) model. Generally, a fuzzy cellular neural network (FCNN) is a combination of fuzzy logic and a cellular neural network. Fuzzy logic models are composed of input and output templates which are in the form of a sum of product operations that help to evaluate the information transmission on a rule-basis. Hence, this study proposes a user-controlled FICNNs model with the same dynamic properties as FICNN model. In this regard, the synchronization approach is considerably effective in ensuring the dynamical properties of the drive (without control input) and response (with external control input). Theoretically, the synchronization between the drive-response can be ensured by analyzing the error model derived from the drive-response but due to nonlinearities, the Lyapunov stability theory can be utilized to derive sufficient stability conditions in terms of linear matrix inequalities (LMIs) that will guarantee the convergence of the error model onto the origin. Distinct from the existing stability conditions, this paper derives the stability conditions by involving the delay information in the form of a quadratic function with lower and upper bounds, which are evaluated through the negative determination lemma (NDL). Besides, numerical simulations that support the validation of proposed theoretical frameworks are discussed. As a direct application, the FICNN model is considered as a cryptosystem in image encryption and decryption algorithm, and the corresponding outcomes are illustrated along with security measures.

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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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