Anti-windup compensation design for a class of distributed time-delayed cellular neural networks

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.

Robust Sliding Mode Control for Nonlinear Discrete-Time Delayed Systems Based on Neural Network

This paper presents a robust sliding mode controller for a class of unknown nonlinear discrete-time systems in the presence of fixed time delay. A neural-network approximation and the Lyapunov-Krasovskii functional theory into the sliding-mode technique is used and a neural-network based sliding mode control scheme is proposed. Because of the novality of Chebyshev Neural Networks (CNNs), that it requires much less computation time as compare to multi layer neural network (MLNN), is preferred to approximate the unknown system functions. By means of linear matrix inequalities, a sufficient condition is derived to ensure the asymptotic stability such that the sliding mode dynamics is restricted to the defined sliding surface. The proposed sliding mode control technique guarantees the system state trajectory to the designed sliding surface. Finally, simulation results illustrate the main characteristics and performance of the proposed approach.

Modeling of Trophospheric Ozone Concentrations Using Genetically Trained Multi-Level Cellular Neural Networks

Tropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul are utilized in constituting the model. A supervised algorithm for the evaluation of ozone concentration using a genetically trained multi-level cellular neural network （ML-CNN） is introduced, developed, and applied to real data. A genetic algorithm is used in the optimization of CNN templates. The model results and the actual measurement results are compared and statistically evaluated. It is observed that seasonal changes in ozone concentrations are reflected effectively by the concentrations estimated by the multilevel-CNN model structure, with a correlation value of 0.57 ascertained between actual and model results. It is shown that the multilevel-CNN modeling technique is as satisfactory as other modeling techniques in associating the data in a complex medium in air pollution applications.

ON THE STABILITY OF CELLULAR NEURAL NETWORKS WITH FEEDBACK MODE

Cellular Neural Networks (CNN) with feedback mode and M×N cells are equivalent to a network which possesses 2M×N cells, a neighborhood with mirror-like structure, space-variant templates and without feedback as well as without input templates. The stability of the CNN with feedback mode and transformations with the neighborhood of mirror-like structure are discussed.

Exponential stability of cellular neural networks with multiple time delays and impulsive effects

In this work, the stability issues of the equilibrium points of the cellular neural networks with multiple time delays and impulsive effects are investigated. Based on the stability theory of Lyapunov-Krasovskii, the method of linear matrix inequality （LMI） and parametrized first-order model transformation, several novel conditions guaranteeing the delaydependent and the delay-independent exponential stabilities are obtained. A numerical example is given to illustrate the effectiveness of our results.

Initial Object Segmentation for Video Object Plane Generation Using Cellular Neural Networks

MPEG 4 is a basic tool for interactivity and manipulation of video sequences. Video object segmentation is a key issue in defining the content of any video sequence, which is often divided into two steps: initial object segmentation and object tracking. In this paper, an initial object segmentation method for video object plane(VOP) generation using color information is proposed. Based on 3 by 3 linear templates, a cellular neural network (CNN) is used to implemented object segmentation. The Experimental results are presented to verify the efficiency and robustness of this approach.

Development of a convolutional neural network based geomechanical upscaling technique for heterogeneous geological reservoir

Geomechanical assessment using coupled reservoir-geomechanical simulation is becoming increasingly important for analyzing the potential geomechanical risks in subsurface geological developments.However,a robust and efficient geomechanical upscaling technique for heterogeneous geological reservoirs is lacking to advance the applications of three-dimensional(3D)reservoir-scale geomechanical simulation considering detailed geological heterogeneities.Here,we develop convolutional neural network(CNN)proxies that reproduce the anisotropic nonlinear geomechanical response caused by lithological heterogeneity,and compute upscaled geomechanical properties from CNN proxies.The CNN proxies are trained using a large dataset of randomly generated spatially correlated sand-shale realizations as inputs and simulation results of their macroscopic geomechanical response as outputs.The trained CNN models can provide the upscaled shear strength(R^(2)>0.949),stress-strain behavior(R^(2)>0.925),and volumetric strain changes(R^(2)>0.958)that highly agree with the numerical simulation results while saving over two orders of magnitude of computational time.This is a major advantage in computing the upscaled geomechanical properties directly from geological realizations without the need to perform local numerical simulations to obtain the geomechanical response.The proposed CNN proxybased upscaling technique has the ability to(1)bridge the gap between the fine-scale geocellular models considering geological uncertainties and computationally efficient geomechanical models used to assess the geomechanical risks of large-scale subsurface development,and(2)improve the efficiency of numerical upscaling techniques that rely on local numerical simulations,leading to significantly increased computational time for uncertainty quantification using numerous geological realizations.

A new neural network model for the feedback stabilization of nonlinear systems

A new neural network model termed ‘standard neural network model’ (SNNM) is presented, and a state-feedback control law is then designed for the SNNM to stabilize the closed-loop system. The control design constraints are shown to be a set of linear matrix inequalities (LMIs), which can be easily solved by the MATLAB LMI Control Toolbox to determine the control law. Most recurrent neural networks (including the chaotic neural network) and nonlinear systems modeled by neural networks or Takagi and Sugeno (T-S) fuzzy models can be transformed into the SNNMs to be stabilization controllers synthesized in the framework of a unified SNNM. Finally, three numerical examples are provided to illustrate the design developed in this paper.

基于CNN彩色图像边缘检测的车牌定位方法

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.

CNN-PDE非线性图像滤波器

偏微分(PDE)非线性图像滤波方法具有优良特性，但由于其计算量大而无法满足实时控制需求．细胞神经网(CNN)可以描述图像PDE模型，利用模拟CNN芯片并行求解，有助于提高其实时性．本文用CNN实现了PDE偏差非线性图像滤波器，提出了一种局部运算的噪声估计方法以选择适当的平滑系数．计算结果表明，这种噪声估计方法可以对不同噪声水平作出较精确的估计．仿真实验结果表明，CNN-PDE非线性滤波器取得了满意的滤波效果，用CNN实现PDE非线性滤波器的方法是有效可行的．

基于CNN和小波变换的数字水印技术

本文采用5涡卷CNN混沌电路方程产生混沌信号,编码后再转换成二维的混沌二值图像,并以此作为密钥对水印图置乱,再嵌入到宿主图的小波系数中去,通过实验结果还分析了这种方法的信噪比和相关度。

基于参数自适应CNN的灰度图像边缘检测

边缘是图像的重要特征。在应用细胞神经网络提取图像边缘时,网络的稳定性和参数的选择是关键。文中推导了细胞神经网络的稳定条件,并提出了网络参数的自适应设计思路。基于Matlab7.0平台,通过编写仿真程序,检测灰度图像边缘,得到良好效果。实验证明,该法还能有效抑制噪声的干扰。

基于五阶CNN的图像边检测算法研究

边缘是图像的最基本的特征之一,边缘提取是图像分析中非常重要的步骤,而细胞神经网络是边缘检测中很有效的一种方法.作者基于细胞神经网络(cellular neural network,简称CNN),研究了5阶CNN模板对图像边缘检测的过程,阐述了算法实现过程中的关键步骤,并且证明了算法的稳定性.对图像分别采用基于5阶、3阶CNN算法和经典算子(Prewit、Canny、Sobel等)进行边缘提取,定性分析比较了几类算法在性能上的优劣,定量比较了检测结果的准确性.实验结果表明,基于5阶CNN模板算法的边缘检测结果更加显著,且在硬件实现上能够高速并行计算,实现图像实时处理.

基于CNN的康普顿背散射图像中违禁品分割方法

康普顿背散射(CBS)技术是一项较新的射线安检技术,可以提高复杂背景下安检设备对爆炸物等有机违禁品的探测力度,这其中一个重要环节就是图像中违禁品的分割问题.本文提出了一种基于细胞神经网络(CNN)的CBS图像滤波及分割方法,在此基础上又提出了一种基于CNN和数学形态学的孤立点滤除方法,并对这两种方法进行了详细分析,给出了实例的仿真结果,验证了方法的有效性.本文提出的方法为并行处理算法,易于大规模集成电路(VLSI)实现,满足安检设备对CBS图像处理的实时性要求.

基于CNN的分块自适应彩色图像边缘检测的研究

利用细胞神经网络(CNN)模型对彩色图像边缘检测时,首先要解决彩色空间的选择以及颜色距离的计算问题,其次网络参数的选择也是一个重要问题。为了达到在确保边缘检测准确的同时有效抑制噪声的目的,对整幅图像进行分块自适应检测,采用熵来度量图像的各个子区域的不同性质,然后根据该区域的性质选择一组合适的网络参数,对提取该区域图像边缘的CNN模板进行了理论分析和鲁棒性研究,提出一个设计符合相应功能要求的CNN鲁棒性定理,它为设计相应的CNN模板参数提供了解析判据。仿真实验表明,该算法具有较好的健壮性。

CNN的全局渐近稳定性分析与改进

研究一类通用细胞神经网络的稳定性问题。采用Lipschitiz连续性条件证明了系统平衡点的存在性,利用Lyapunov函数稳定性分析方法结合不等式分析,给出系统平衡点唯一和全局渐近稳定的充分条件,该条件推广并改进了已有结论,具有更好的通用性,经实验仿真是可行的。

基于CNN通用编程的字母识别研究

利用CNN处理器的并行图像处理原理和通用编程的理论,提出基于CNN二值图像字母识别的通用编程方法,说明了用该方法作实时图像处理与识别的操作过程,解决了传统计算机串行数据处理的瓶颈问题.

基于QCNN的非线性跟踪问题研究

针对如何快速准确地跟踪到非线性系统的状态问题,研究了量子细胞神经网络(QCNN)在非线性跟踪中的应用。在满足Lyapunov函数指数收敛的条件下,设计了一种新型参数形式的控制器,在此基础上,对三种非线性系统即确定性非线性运动、参数和运动规律未知的非线性数据系统以及典型蔡氏电路进行了QCNN跟踪研究。仿真结果表明,在QCNN系统中,通过设计合理的控制器可以实现非线性问题状态的有效跟踪,且实验结果为QCNN系统复杂度与跟踪的及时性之间关系提供了参考依据和有力的说明。设计的新型控制器及对实际问题处理方法为QCNN的理论及应用研究具有借鉴意义。

基于二次型的CNN全局渐近稳定性研究

细胞神经网络稳定性目前已经在图像处理、视频通信和最优控制等领域得到了一定的应用,因此进行稳定性的研究具有重要的意义,如何选择合理的参数模板是研究稳定性的关键问题。运用Lyapunov第二方法对细胞神经网络的全局渐近稳定性进行分析,通过构造出一个较好的Lyapunov函数来得到判定系统全局渐近稳定的一组新的充分条件。该条件改进了已有的结论,进一步推导和完善了系统全局渐近稳定平衡点为原点时的充分条件,经过数值仿真实验验证了其有效性和可行性。

基于DCT和CNN混沌系统的彩色数字水印加密新算法

数字水印技术作为抵抗多媒体盗版的最后一道技术防线,具有广泛的应用前景和巨大的经济价值。基于离散余弦变换(DCT)以及细胞神经网络(CNN)混沌理论提出了一种数字水印加密新算法。算法分两步进行,首先是利用5阶细胞神经网络混沌系统产生的随机序列辅助某种运算对彩色水印图像加密,然后利用分块离散余弦变换将加密以后的彩色水印图像嵌入到载体彩色图像中,以此来实现水印加密以及嵌入的过程。在仿真实验基础上,通过指标PSNR和NC的定量分析,结果证明新算法具有较强鲁棒性,不可感知性和安全性。