负阻态忆阻Hopfield神经网络动力学  

Dynamics of Negative Resistive Memristive Hopfield Neural Networks

作　　者：刘益安 马瑞辰 李国[1] 于奇[1] 刘洋[1] 胡绍刚[1] LIU Yian;MA Ruichen;LI Guo;YU Qi;LIU Yang;HU Shaogang(School of Electronic Science and Engineering,University of Electronic Science and Technology of China Chengdu 611731;Chongqing Institute of Microelectronics Industry Technology,University of Electronic Science and Technology of China Gaoxin Chongqing 401332)

机构地区：[1]电子科技大学电子科学与工程学院,成都611731 [2]电子科技大学重庆微电子产业技术研究院,重庆高新区401332

出　　处：《电子科技大学学报》2023年第1期38-43,共6页Journal of University of Electronic Science and Technology of China

基　　金：国家自然科学基金(92064004);重庆市技术创新与应用发展重点项目(cstc2021jscx-gksb0114)。

摘　　要：人类大脑是一个高度复杂且规模庞大的非线性动力学系统,其动力学行为与人类智能活动密切相关。基于忆阻器的人工神经网络不仅可以很好地模拟人脑工作机制,而且其非线性特性可以为神经网络带来更为丰富的动力学行为。为了进一步发挥神经网络的优势,引入一种新的具有负阻态功能的忆阻器模型,该模型打破了原有忆阻器的阻态极性限制,为忆阻器扮演神经网络突触仿生器件提供了更加丰富的变化性能。在对忆阻器模型分析的基础上,提出了一种新的忆阻Hopfield神经网络(HNN),进一步加强了HNN的负反馈功能,使之表现出更加丰富和复杂的动力学行为。实验结果表明,新忆阻HNN拥有较为丰富的动力学行为,具有一定的混沌特性。在不同的忆阻器参数以及权值矩阵取值条件下,观察系统的相位轨迹图、Lyapunov指数的变化情况,并与同类型网络进行对比,进一步证明提出的神经网络的有效性,同时复杂的动力学特性也为在数据处理、图像加密等方面的应用提供了研究支撑。The human brain is a highly complex and large-scale nonlinear dynamic system,and its dynamic behavior is closely related to human intelligent activities.The artificial neural network based on memristors can not only better simulate the working mechanism of human brain,but also its nonlinear characteristics can bring richer dynamic behavior to the neural network.In order to further exploit the advantages of neural networks,a new memristor model with negative resistance is introduced in this paper.This model breaks the restriction of the resistance state polarity of the original memristor,and provides a richer variety of performance for the memristor to act as a neural network synaptic bionic device.A new Hopfield neural network(HNN)based on the memristor model is constructed,which further strengthens the negative feedback function of the Hopfield neural network and makes it exhibit richer and more complex dynamic behaviors.The experimental results show that the new memristive Hopfield neural network has rich dynamic behavior characteristics and some chaotic phenomena.Under the conditions of different values of memristor’s parameters and weight matrix,the changes of phase trajectory and Lyapunov exponent of the system are observed,and comparison with the same type of networks are done,which further proves the effectiveness of the proposed neural network.At the same time,the complex dynamic characteristics also provide research support for applications in data processing and image encryption.

关 键 词：动力学 HOPFIELD神经网络 忆阻器 负阻态 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]