基于单电子器件的细胞神经网络实现及应用研究

利用单电子晶体管和互补型金属氧化物半导体场效应晶体管的混合结构所具有的负微分电阻特性实现了细胞神经网络(CNN),设计构成了CNN的细胞体电路、A模板电路和B模板电路,并将构成的CNN用于图像处理应用研究中.仿真结果表明,所设计的硬件电路具有结构简单、功耗低、响应速度快等特点,可用于构成各种规模的CNN,进一步提高集成电路的集成度. 关键词： 单电子晶体管 细胞神经网络 负微分电阻     

改进型细胞神经网络实现的忆阻器混沌电路

在通用细胞神经网络单元的基础上,本文利用忆阻器本身的非线性实现了一种改进型细胞神经网络单元.通过连接4个改进的细胞神经网络单元导出了一种基于状态控制细胞神经网络的忆阻器混沌电路.为了验证该方法的正确性,采用通用的电子器件构建了一个忆阻器的模拟等效电路,并对提出的电路进行了实验分析.实验结果与数值仿真结果的一致性表明采用改进型的细胞神经网络可以有效地实现忆阻器混沌电路.     

基于单电子晶体管 - 金属氧化物场效应晶体管电路的离散混沌系统实现

提出了一种利用单电子晶体管与金属氧化物半导体的混合结构(SET-MOS)实现离散混沌系统的方法.研究了两个并联结构的单电子晶体管在电流源偏置下的传输特性,并建立其相应的S形分段线性函数模型.基于该模型实现了一维离散映射系统,分析了它的动力学特性,包括一维映射过程、分岔图和Lyapunov指数等.最后利用SET-MOS混合电路设计出该离散混沌系统的电子电路,验证了理论分析和实现方法的正确性.研究结果表明,该方法不仅可行,而且物理实现结构简单,利于集成. 关键词： 离散映射 Lyapunov指数 分岔 电路实现     

基于单电子器件的混沌电路研究

利用单电子晶体管和金属氧化物半导体的混合器件——SETMOS设计实现了细胞神经网络结构的蔡氏电路,得到了单涡卷吸引子和双涡卷吸引子.通过设计构成的SETMOS跨导放大器和SETMOS电压比较器,提出了基于SETMOS的类双涡卷混沌电路,仿真验证了类双涡卷吸引子.模拟结果表明,所设计的硬件电路结构简单,功耗低,有利于进一步提高集成电路的集成度,为混沌在工程领域的实际应用提供了新方法. 关键词： 细胞神经网络 蔡氏电路 混沌 涡卷     

基于CNN的红外图像边缘检测算法的FPGA实现

提出了一种以现场可编程门阵列为硬件处理器实现基于细胞神经网络的红外图像边缘检测方法.首先利用simulink的算法行为特性搭建红外图像输入模块,获得相关的红外图像头信息并对红外图像像素值范围进行相应变化,然后根据细胞神经网络模板所创建的查找表设计单个细胞元软核,再利用细胞神经网络阵列的规则性和互联的局域性,将单个细胞元软核扩展成细胞神经网络阵列.最后采用modelsim将细胞神经网络阵列与红外图像输入、输出模块相关联,从而达到实时处理的效果.实验结果表明:基于现场可编程门阵列为硬件处理器平台实现的细胞神经网络对红外图像进行边缘检测取得了较好的效果,且与MATLAB软件仿真的结果进行对比得出两者只有极其微小的差别.在Xilinx公司Virtex-6系列的现场可编程门阵列平台上,综合后占用极少资源的情况下得到142.693 MHz的最高频率,并且达到了2.378 Mpixels/sec处理速度.     

基于CNN的红外图像边缘检测算法的FPGA实现

提出了一种以现场可编程门阵列为硬件处理器实现基于细胞神经网络的红外图像边缘检测方法.首先利用simulink的算法行为特性搭建红外图像输入模块,获得相关的红外图像头信息并对红外图像像素值范围进行相应变化,然后根据细胞神经网络模板所创建的查找表设计单个细胞元软核,再利用细胞神经网络阵列的规则性和互联的局域性,将单个细胞元软核扩展成细胞神经网络阵列.最后采用modelsim将细胞神经网络阵列与红外图像输入、输出模块相关联,从而达到实时处理的效果.实验结果表明:基于现场可编程门阵列为硬件处理器平台实现的细胞神经网络对红外图像进行边缘检测取得了较好的效果,且与MATLAB软件仿真的结果进行对比得出两者只有极其微小的差别.在Xilinx公司Virtex-6系列的现场可编程门阵列平台上,综合后占用极少资源的情况下得到142.693 MHz的最高频率,并且达到了2.378 Mpixels/sec处理速度.     

亚像元成像系统细胞神经网络插值方法研究

亚像元动态成像技术是实现卫星相机小型化及提高相机空间分辨率的有效方法。采用细胞神经网络(CNN)实现了对亚像元图像的实时插值,对插值的过程进行了详细的分析,并与基于亚像元图像的传统插值方法进行了对比。结果表明,亚像元成像系统细胞神经网络插值方法不仅可以节约系统资源,而且还可以提高系统的运行速度。通过仿真证明了该方法的正确性。     

车辆自动变速器电控单元的CAN接口设计

采用飞思卡尔公司的MC9S12DP256单片机内部集成的CAN(Controller Area Network)模块设计了车辆自动变速器电控单元的CAN通信系统,设计了相应的硬件接口电路和软件,实现了车辆自动变速器电控单元与电喷发动机和ABS电控单元之间的通信.     

面向神经形态感知和计算的柔性忆阻器基脉冲神经元

受人脑工作模式的启发,脉冲神经元作为人工感知系统和神经形态计算体系的基本计算单元发挥着重要作用.然而,基于传统互补金属氧化物半导体技术的神经元电路结构复杂,功耗高,且缺乏柔韧性,不利于大规模集成和与人体兼容的柔性感知系统的应用.本文制备的柔性忆阻器展示出了稳定的阈值转变特性和优异的机械弯折特性,其弯折半径可达1.5 mm,弯折次数可达10~4次.基于此器件构建的神经元电路实现了神经元的关键积分放电特性,且其频率-输入电压关系具有整流线性单元相似性,可实现基于转换法的脉冲神经网络中神经元的非线性处理功能.此外,基于电子传输机制和构建的核壳模型,对柔性忆阻器的工作机制进行分析,提出了电场和热激发主导的阈值转变机制;进一步对忆阻器和神经元的电学特性进行电路仿真模拟,验证了柔性忆阻器和神经元电路工作机制的合理性.本文对柔性神经元的研究可为神经形态感知和计算系统的构建提供硬件基础和理论指导.     

基于混合神经网络和注意力机制的混沌时间序列预测

为提高混沌时间序列的预测精度,提出一种基于混合神经网络和注意力机制的预测模型(Att-CNNLSTM),首先对混沌时间序列进行相空间重构和数据归一化,然后利用卷积神经网络(CNN)对时间序列的重构相空间进行空间特征提取,再将CNN提取的特征和原时间序列组合,用长短期记忆网络(LSTM)根据空间特征提取时间特征,最后通过注意力机制捕获时间序列的关键时空特征,给出最终预测结果.将该模型对Logistic,Lorenz和太阳黑子混沌时间序列进行预测实验,并与未引入注意力机制的CNN-LSTM模型、单一的CNN和LSTM网络模型、以及传统的机器学习算法最小二乘支持向量机(LSSVM)的预测性能进行比较.实验结果显示本文提出的预测模型预测误差低于其他模型,预测精度更高.     

Widrow-cellular neural network and optoelectronic implementation

A new type of optoelectronic cellular neural network has been developed by providing the capability of coefficients adjusment of cellular neural network (CNN) using Widrow based perceptron learning algorithm. The new supervised cellular neural network is called Widrow-CNN. Despite the unsupervised CNN, the proposed learning algorithm allows to use the Widrow-CNN for various image processing applications easily. Also, the capability of CNN for image processing and feature extraction has been improved using basic joint transform correlation architecture. This hardware application presents high speed processing capability compared to digital applications. The optoelectronic Widrow-CNN has been tested for classic CNN feature extraction problems. It yields the best results even in case of hard feature extraction problems such as diagonal line detection and vertical line determination.     

Chaotic behavior in noninteger-order cellular neural networks

In this paper, a simple system showing chaotic behavior is introduced. It is based on the well-known concept of cellular neural networks (CNNs), which have already given good results in generating complex dynamics. The peculiarity of the CNN model consists in the fact that it replaces the traditional first-order cell with a noninteger-order one. The introduction of the fractional cell, with a suitable choice of the coupling parameters, leads to the onset of chaos in a simple two-cell system. A theoretical approach, based on the harmonic balance theory, has been used to investigate the existence of chaos. A circuit realization of the proposed fractional two-cell chaotic CNN is reported and the corresponding strange attractor is also shown.     

SETMOS实现多涡卷蔡氏电路的研究

基于细胞神经网络结构,利用具有负微分电阻特性的单电子晶体管与金属氧化物半导体混合结构器件SETMOS实现了多涡卷蔡氏电路.对该电路系统的基本动力学特性(如相图、分岔图、Lyapunov指数、Poincaré映射和功率谱)进行了理论分析和数值仿真,并利用电路仿真实验验证了该三阶四涡卷蔡氏电路设计的正确性和可行性.研究结果表明,SETMOS的负微分电阻特性决定着多涡卷蔡氏电路的复杂动力学行为,而且所设计的电路结构简单易行.     

Image noise cancellation using linear matrix inequality and cellular neural network

In this paper, the technique of image noise cancellation is presented by employing cellular neural networks (CNN) and linear matrix inequality (LMI). The main objective is to obtain the templates of CNN by using a corrupted image and a corresponding desired image. A criterion for the uniqueness and global asymptotic stability of the equilibrium point of CNN is presented based on the Lyapunov stability theorem (i.e., the feedback template “A” of CNN is solved at this step), and the input template “B” of CNN is designed to achieve desirable output by using the property of saturation nonlinearity of CNN. It is shown that the problem of image noise cancellation can be characterized in terms of LMIs. The simulation results indicate that the proposed method is useful for practical application.     

A Signal processing method of OFDM communication receiver based on CNN

Orthogonal frequency division multiplexing (OFDM) the signal processing is a key issue in wireless communication research. The multipath effect and Doppler shift of wireless communication channels can lead to distortion of the transmitted signal, which poses a considerable challenge to the information recovery of communication receivers. This paper presents the signal processing method of OFDM communication based on convolutional neural network (CNN). The method replaces all signal processing modules of the OFDM communication receiver with CNN, and the information is recovered by the CNN. In order to adapt to the processing of communication signals, we designed a one-dimensional convolutional neural network (1D-CONV-CNN) model as the neural network structures by this method. Simulation results indicate that the signal processing method effectively reduces the bit error rate (BER) and improves its performance compared with the conventional reception method under different channel conditions.     

Complex dynamic behaviors in hyperbolic-type memristor-based cellular neural network

This paper presents a new hyperbolic-type memristor model,whose frequency-dependent pinched hysteresis loops and equivalent circuit are tested by numerical simulations and analog integrated operational amplifier circuits.Based on the hyperbolic-type memristor model,we design a cellular neural network(CNN)with 3-neurons,whose characteristics are analyzed by bifurcations,basins of attraction,complexity analysis,and circuit simulations.We find that the memristive CNN can exhibit some complex dynamic behaviors,including multi-equilibrium points,state-dependent bifurcations,various coexisting chaotic and periodic attractors,and offset of the positions of attractors.By calculating the complexity of the memristor-based CNN system through the spectral entropy(SE)analysis,it can be seen that the complexity curve is consistent with the Lyapunov exponent spectrum,i.e.,when the system is in the chaotic state,its SE complexity is higher,while when the system is in the periodic state,its SE complexity is lower.Finally,the realizability and chaotic characteristics of the memristive CNN system are verified by an analog circuit simulation experiment.