Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application t...Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application to edge detection.An MCM-CNN is designed by adopting a memristor crossbar composed of a pair of memristors.MCM-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and compactation.Figure of merit(FOM)is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection results.Experiment results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.展开更多
A new sufficient conditions for the global exponential stability of the equilibrium point for delayed cellular neural networks （DCNNs） is presented. It is shown that the use of a more general type of Lyapunov-Krasov...A new sufficient conditions for the global exponential stability of the equilibrium point for delayed cellular neural networks （DCNNs） is presented. It is shown that the use of a more general type of Lyapunov-Krasovskii function enables the derivation of new results for an exponential stability of the equilibrium point for DCNNs. The results establish a relation between the delay time and the parameters of the network. The results are also compared with one of the most recent results derived in the literature.展开更多
Both time-delays and anti-windup(AW)problems are conventional problems in system design,which are scarcely discussed in cellular neural networks(CNNs).This paper discusses stabilization for a class of distributed time...Both time-delays and anti-windup(AW)problems are conventional problems in system design,which are scarcely discussed in cellular neural networks(CNNs).This paper discusses stabilization for a class of distributed time-delayed CNNs with input saturation.Based on the Lyapunov theory and the Schur complement principle,a bilinear matrix inequality(BMI)criterion is designed to stabilize the system with input saturation.By matrix congruent transformation,the BMI control criterion can be changed into linear matrix inequality(LMI)criterion,then it can be easily solved by the computer.It is a one-step AW strategy that the feedback compensator and the AW compensator can be determined simultaneously.The attraction domain and its optimization are also discussed.The structure of CNNs with both constant timedelays and distribute time-delays is more general.This method is simple and systematic,allowing dealing with a large class of such systems whose excitation satisfies the Lipschitz condition.The simulation results verify the effectiveness and feasibility of the proposed method.展开更多
Some sufficient conditions for the global exponential stability and lower bounds on the rate of exponential convergence of the cellular neural networks with delay (DCNNs) are obtained by means of a method based on del...Some sufficient conditions for the global exponential stability and lower bounds on the rate of exponential convergence of the cellular neural networks with delay (DCNNs) are obtained by means of a method based on delay differential inequality. The method, which does not make use of any Lyapunov functional, is simple and valid for the stability analysis of neural networks with delay. Some previously established results in this paper are shown to be special casses of the presented result.展开更多
The dynamic behavior of discrete-time cellular neural networks(DTCNN), which is strict with zero threshold value, is mainly studied in asynchronous mode and in synchronous mode. In general, a k-attractor of DTCNN is n...The dynamic behavior of discrete-time cellular neural networks(DTCNN), which is strict with zero threshold value, is mainly studied in asynchronous mode and in synchronous mode. In general, a k-attractor of DTCNN is not a convergent point. But in this paper, it is proved that a k-attractor is a convergent point if the strict DTCNN satisfies some conditions. The attraction basin of the strict DTCNN is studied, one example is given to illustrate the previous conclusions to be wrong, and several results are presented. The obtained results on k-attractor and attraction basin not only correct the previous results, but also provide a theoretical foundation of performance analysis and new applications of the DTCNN.展开更多
The global asymptotic stability of cellular neural networks with delays is investigated. Three kinds of time delays have been considered. New delay-dependent stability criteria are proposed and are formulated as the f...The global asymptotic stability of cellular neural networks with delays is investigated. Three kinds of time delays have been considered. New delay-dependent stability criteria are proposed and are formulated as the feasibility of some linear matrix inequalities, which can be checked easily by resorting to the recently developed interior-point algorithms. Based on the Finsler Lemma, it is theoretically proved that the proposed stability criteria are less conservative than some existing results.展开更多
在移动通信网络快速发展的背景下,蜂窝流量预测对于网络规划、优化和资源管理具有重大意义。针对蜂窝流量数据的复杂性和非线性特点,提出一种基于二次分解的混合神经网络蜂窝流量预测方法。首先,采用自适应噪声的完备集合经验模式分解(c...在移动通信网络快速发展的背景下,蜂窝流量预测对于网络规划、优化和资源管理具有重大意义。针对蜂窝流量数据的复杂性和非线性特点,提出一种基于二次分解的混合神经网络蜂窝流量预测方法。首先,采用自适应噪声的完备集合经验模式分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)方法将原始流量分解为多个子序列,利用K-Shape聚类算法重构为频率序列和趋势序列。为了更细致地揭示数据的内在结构,运用变分模态分解(variational mode decomposition,VMD)方法对频率序列进行二次分解,生成多维频率序列。然后,将一维趋势序列和多维频率序列分别输入至局部特征提取模块,其中单通道特征提取层利用一维卷积神经网络(one-dimensional convolution neural network,1DCNN)提取一维趋势序列的局部特征,而多通道特征提取层则结合卷积块注意力模块(convolutional block attention module,CBAM)捕捉多维频率序列中的关键信息。紧接着将提取到的特征向量分别输入到时序信息学习模块中,利用双向长短时记忆(bidirectional long short term memory,BiLSTM)网络和注意力机制学习时序变化规律,完成预测流量的输出。最后,通过对趋势序列和频率序列的预测结果求和,实现对蜂窝流量的准确预测。为了验证所提方法的有效性,利用公开数据集进行实验验证,并与多种不同方法进行对比。实验结果表明,所提预测方法展现出更优的预测性能,为蜂窝网络的智能管理和优化提供了有力支持。展开更多
针对现有车牌定位算法准确率不高、步骤多和速度慢等问题,提出一种彩色图像车牌定位方法(License plate locating based on CNN color edge detec tion,LPLCCED).首先利用细胞神经网络(Cell neural network,CNN)模型导出一种与车牌颜色...针对现有车牌定位算法准确率不高、步骤多和速度慢等问题,提出一种彩色图像车牌定位方法(License plate locating based on CNN color edge detec tion,LPLCCED).首先利用细胞神经网络(Cell neural network,CNN)模型导出一种与车牌颜色特征相结合的车牌定位专用边缘检测算法,将车牌的颜色对约束条件融合到边缘检测算法中,本文专用边缘检测算法可以大大缩小车牌初步定位的范围.接下来提出一种针对车牌特征的边缘滤波算法,最后根据车牌结构和纹理特征对候选区域进行判别验证.该流程的各个环节都可以通过硬件实现,为面向智能交通领域的实时车牌识别系统的前期车牌定位处理提供了依据.展开更多
基金supported by the Research Fund for International Young Scientists of the National Natural Science Foundation of China(61550110248)the Research on Fundamental Theory of Shared Intelligent Street Lamp for New Scene Service(H04W200495)+1 种基金Sichuan Science and Technology Program(2019YFG0190)the Research on Sino-Tibetan Multi-source Information Acquisition,Fusion,Data Mining and its Application(H04W170186).
文摘Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application to edge detection.An MCM-CNN is designed by adopting a memristor crossbar composed of a pair of memristors.MCM-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and compactation.Figure of merit(FOM)is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection results.Experiment results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.
基金This project was supported in part by the National Natural Science Foundation of China (60404022, 60604004)the Key Scientific Research project of Education Ministry of China (204014)the National Natural Science Foundation of China for Distinguished Young Scholars (60525303).
文摘A new sufficient conditions for the global exponential stability of the equilibrium point for delayed cellular neural networks （DCNNs） is presented. It is shown that the use of a more general type of Lyapunov-Krasovskii function enables the derivation of new results for an exponential stability of the equilibrium point for DCNNs. The results establish a relation between the delay time and the parameters of the network. The results are also compared with one of the most recent results derived in the literature.
基金supported by the National Natural Science Foundation of China(61374003 41631072)the Academic Foundation of Naval University of Engineering(20161475)
文摘Both time-delays and anti-windup(AW)problems are conventional problems in system design,which are scarcely discussed in cellular neural networks(CNNs).This paper discusses stabilization for a class of distributed time-delayed CNNs with input saturation.Based on the Lyapunov theory and the Schur complement principle,a bilinear matrix inequality(BMI)criterion is designed to stabilize the system with input saturation.By matrix congruent transformation,the BMI control criterion can be changed into linear matrix inequality(LMI)criterion,then it can be easily solved by the computer.It is a one-step AW strategy that the feedback compensator and the AW compensator can be determined simultaneously.The attraction domain and its optimization are also discussed.The structure of CNNs with both constant timedelays and distribute time-delays is more general.This method is simple and systematic,allowing dealing with a large class of such systems whose excitation satisfies the Lipschitz condition.The simulation results verify the effectiveness and feasibility of the proposed method.
文摘Some sufficient conditions for the global exponential stability and lower bounds on the rate of exponential convergence of the cellular neural networks with delay (DCNNs) are obtained by means of a method based on delay differential inequality. The method, which does not make use of any Lyapunov functional, is simple and valid for the stability analysis of neural networks with delay. Some previously established results in this paper are shown to be special casses of the presented result.
文摘The dynamic behavior of discrete-time cellular neural networks(DTCNN), which is strict with zero threshold value, is mainly studied in asynchronous mode and in synchronous mode. In general, a k-attractor of DTCNN is not a convergent point. But in this paper, it is proved that a k-attractor is a convergent point if the strict DTCNN satisfies some conditions. The attraction basin of the strict DTCNN is studied, one example is given to illustrate the previous conclusions to be wrong, and several results are presented. The obtained results on k-attractor and attraction basin not only correct the previous results, but also provide a theoretical foundation of performance analysis and new applications of the DTCNN.
基金supported by the National Natural Science Foundation of China (60604017) the Natural Science Foundation of Zhejiang Province (Y107657).
文摘The global asymptotic stability of cellular neural networks with delays is investigated. Three kinds of time delays have been considered. New delay-dependent stability criteria are proposed and are formulated as the feasibility of some linear matrix inequalities, which can be checked easily by resorting to the recently developed interior-point algorithms. Based on the Finsler Lemma, it is theoretically proved that the proposed stability criteria are less conservative than some existing results.
文摘在移动通信网络快速发展的背景下,蜂窝流量预测对于网络规划、优化和资源管理具有重大意义。针对蜂窝流量数据的复杂性和非线性特点,提出一种基于二次分解的混合神经网络蜂窝流量预测方法。首先,采用自适应噪声的完备集合经验模式分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)方法将原始流量分解为多个子序列,利用K-Shape聚类算法重构为频率序列和趋势序列。为了更细致地揭示数据的内在结构,运用变分模态分解(variational mode decomposition,VMD)方法对频率序列进行二次分解,生成多维频率序列。然后,将一维趋势序列和多维频率序列分别输入至局部特征提取模块,其中单通道特征提取层利用一维卷积神经网络(one-dimensional convolution neural network,1DCNN)提取一维趋势序列的局部特征,而多通道特征提取层则结合卷积块注意力模块(convolutional block attention module,CBAM)捕捉多维频率序列中的关键信息。紧接着将提取到的特征向量分别输入到时序信息学习模块中,利用双向长短时记忆(bidirectional long short term memory,BiLSTM)网络和注意力机制学习时序变化规律,完成预测流量的输出。最后,通过对趋势序列和频率序列的预测结果求和,实现对蜂窝流量的准确预测。为了验证所提方法的有效性,利用公开数据集进行实验验证,并与多种不同方法进行对比。实验结果表明,所提预测方法展现出更优的预测性能,为蜂窝网络的智能管理和优化提供了有力支持。
文摘针对现有车牌定位算法准确率不高、步骤多和速度慢等问题,提出一种彩色图像车牌定位方法(License plate locating based on CNN color edge detec tion,LPLCCED).首先利用细胞神经网络(Cell neural network,CNN)模型导出一种与车牌颜色特征相结合的车牌定位专用边缘检测算法,将车牌的颜色对约束条件融合到边缘检测算法中,本文专用边缘检测算法可以大大缩小车牌初步定位的范围.接下来提出一种针对车牌特征的边缘滤波算法,最后根据车牌结构和纹理特征对候选区域进行判别验证.该流程的各个环节都可以通过硬件实现,为面向智能交通领域的实时车牌识别系统的前期车牌定位处理提供了依据.
