CNN-based difference-controlled adaptive non-linear image filters

In this paper, we develop a common cellular neural network framework for various adaptive non-linear filters based on robust statistic and geometry-driven diffusion paradigms. The base models of both approaches are defined as difference-controlled non-linear CNN templates, while the self-adjusting property is ensured by simple analogic (analog and logic) CNN algorithms. Two adaptive strategies are shown for the order statistic class. When applied to the images distorted by impulse noise both give more visually pleasing results with lower-frequency weighted mean square error than the median base model. Generalizing a variational approach we derive the constrained anisotropic diffusion, where the output of the geometry-driven diffusion model is forced to stay close to a pre-defined morphological constraint. We propose a coarse-grid CNN approach that is capable of calculating an acceptable noise-level estimate (proportional to the variance of the Gaussian noise) and controlling the fine-grid anisotropic diffusion models. A combined geometrical–statistical approach has also been developed for filtering both the impulse and additive Gaussian noise while preserving the image structure. We briefly discuss how these methods can be embedded into a more complex algorithm performing edge detection and image segmentation. The design strategies are analysed primarily from VLSI implementation point of view; therefore all non-linear cell interactions of the CNN architecture are reduced to two fundamental non-linearities, to a sigmoid type and a radial basis function. The proposed non-linear characteristics can be approximated with simple piecewise-linear functions of the voltage difference of neighbouring cells. The simplification makes it possible to convert all space-invariant non-linear templates of this study to a standard instruction set of the CNN Universal Machine, where each instruction is coded by at most a dozen analog numbers. Examples and simulation results are given throughout the text using various intensity images. © 1998 John Wiley & Sons, Ltd.     

Programmable analogue vlsi cnn chip with local digital logic

A new integrated circuit cellular neural network implementation with digitally or continuously selectable template coefficients is presented. Local logic and memory are added into each cell, providing a simple dual (analogue and digital) computing structure. Variable gain OTAs are used as the voltage-controlled current sources to programme the template element values. the cells have local switched feedback (both analogue and digital) to feed from the output to the input or state capacitor. Therefore this analogue array processor can be applied to solve problems with a sequence of different templates. A 4 × 4 CNN circuit is realized using the 2 μm analogue CMOS process.     

Fuzzy Cellular Neural Network: a new paradigm for image processing

A non-linear network structure called the fuzzy cellular neural network (FCNN) is presented. It is a reasonable extension of the cellular neural network (CNN) from classical to fuzzy sets. In this paper, structures of type II FCNNs are presented. A type II FCNN has fuzzy signals and crisp synaptic weights. Some theorems on the dynamical range and equilibrium points of type II FCNNs are presented. Applications of type II FCNNs to min-max medical axis transformation, noise removal and edge detection under a low-SNR condition are presented. Computer simulation results are given. © 1997 by John Wiley & Sons, Ltd.     

Watermarking on CNN‐UM for image and video authentication

In this paper a new approach to fragile watermarking technique is introduced. This problem is particularly interesting in the field of modern multimedia applications, when image and video authentication are required. The approach exploits the cellular automata suitability to work as pseudorandom pattern generators and extends the related algorithms under the framework of the cellular non‐linear networks (CNNs). The result is a novel way to perform watermarking generation in real time, using the presently available CNN‐universal chip prototypes. In this paper, both the CNN algorithms for fragile watermarking as well as on‐chip experimental results are reported, confirming the suitability of CNNs to successfully act as real‐time watermarking generators. The availability of CNN‐based visual microprocessors allows to have powerful algorithms to watermark in real time images or videos for efficient smart camera applications. Copyright © 2004 John Wiley & Sons, Ltd.     

On the universe of stable cellular neural networks

A cellular neural network (CNN) is a novel analogue circuit architecture with many desirable features. This paper extends previous stability results of CNNs to include classes of strictly sign-symmetric and acyclic templates. We show that most of the 3×3 strictly sign-symmetric templates are stable almost everywhere, with the unknown templates reduced to three classes. We also introduce template graphs and CNN graphs and utilize them to obtain results concerning stability and irreducibility of CNN templates.     

基于卷积神经网络多判据融合的井下电网故障选线方法

针对矿井电网消弧线圈接地系统单相接地故障选线方法准确率和可靠性不高的问题,提出基于卷积神经网络多判据融合的选线方法。主要分析了深度学习模型——卷积神经网络的结构与原理,通过快速傅里叶变换和小波变换从故障信息中提取5次谐波分量、小波分析模极大值、衰减直流分量和高频暂态分量作为原始输入数据,并利用改进LeNet-5模型强大的学习能力和泛化能力对其进行融合。基于Matlab软件搭建井下电网仿真模型,结果表明该方法准确性高、可靠性强。     

Cellular neural network realizations of neuron models with diverse spiking patterns

In this paper we show cellular neural network (CNN) models of some basic types of cells characterized by diverse spiking patterns. After presenting some preliminary models (ion channels, neurons), CNN models of action potential generation are given, followed by an analysis of the rate-coding capabilities of the models. Furthermore, we show CNN models of synaptic connections. Finally, CNN models of neurons with diverse intrinsic firing patterns are presented.     

Guiding a mobile robot with cellular neural networks

We show how cellular neural networks (CNNs) are capable of providing the necessary signal processing needed for visual navigation of an autonomous mobile robot. In this way, even complex feature detection and object recognition can be obtained in real time by analogue hardware, making fully autonomous real‐time operation feasible. An autonomous robot was first simulated and then implemented by simulating the CNN with a DSP. The robot is capable of navigating in a maze following lines painted on the floor. Images are processed entirely by a CNN‐based algorithm, and navigation is controlled by a fuzzy‐rule‐based algorithm. Copyright © 2002 John Wiley & Sons, Ltd.     

Compact CMOS implementation of a low‐power,current‐mode programmable cellular neural network

We report on the design and characterization of a full‐analog programmable current‐mode cellular neural network (CNN) in CMOS technology. In the proposed CNN, a novel cell‐core topology, which allows for an easy programming of both feedback and control templates over a wide range of values, including all those required for many signal processing tasks, is employed. The CMOS implementation of this network features both low‐power consumption and small‐area occupation, making it suitable for the realization of large cell‐grid sizes. Device level and Monte Carlo simulations of the network proved that the proposed CNN can be successfully adopted for several applications in both grey‐scale and binary image processing tasks. Results from the characterization of a preliminary CNN test‐chip (8×1 array), intended as a simple demonstrator of the proposed circuit technique, are also reported and discussed. Copyright © 2001 John Wiley & Sons, Ltd.     

Multi‐template approach to realize central pattern generators for artificial locomotion control

Biologically inspired control of artificial locomotion often makes use of the concept of central pattern generator (CPG), a network of neurons establishing the locomotion pattern within a lattice of neural activity. In this paper a new approach, based on cellular neural networks (CNNs), for the design of CPGs is presented. From a biological point of view this new approach includes an approximated chemical synapse realized and implemented in a CNN structure. This allows to extend the results, previously obtained with a reaction‐diffusion‐CNN (RD‐CNN) for the locomotion control of a hexapod robot, to a more general class of artificial CPGs in which the desired locomotion pattern and the switching among patterns are realized by means of a spatio‐temporal algorithm implemented in the same CNN structure. Copyright © 2002 John Wiley & Sons, Ltd.     

A rigorous design method for binary cellular neural networks

In order to be able to take full advantage of the great application potential that lies in cellular neural networks (CNNs) we need to have successful design and learning techniques as well. In almost any analogic CNN algorithm that performs an image processing task, binary CNNs play an important role. We observed that all binary CNNs reported in the literature, except for a connected component detector, exhibit monotonic dynamics. In the paper we show that the local stability of a monotonic binary CNN represents sufficient condition for its functionality, i.e. convergence of all initial states to the prescribed global stable equilibria. Based on this finding, we propose a rigorous design method, which results in a set of design constraints in the form of linear inequalities. These are obtained from simple local rules similar to that in elementary cellular automata without having to worry about continuous dynamics of a CNN. In the end we utilize our method to design a new CNN template for detecting holes in a 2D object. © 1998 John Wiley Sons, Ltd.     

Complete stability of feedback CNNs with dynamic memristors and second‐order cells

The paper considers a feedback cellular neural network (CNN) obtained by interconnecting elementary cells with an ideal capacitor and an ideal flux‐controlled memristor. It is supposed that during the analogue computation of the CNN the memristors behave as dynamic elements, so that each dynamic memristor (DM)‐CNN cell is described by a second‐order differential system in the state variables given by the capacitor voltage and the memristor flux. The proposed networks are called DM‐CNNs, that is CNNs using a dynamic (D) memristor (M). After giving a foundation to the DM‐CNN model, the paper establishes a fundamental result on complete stability, that is convergence of solutions toward equilibrium points, when the DM‐CNN has symmetric interconnections. Because of the presence of dynamic memristors, a DM‐CNN displays peculiar and basically different dynamic properties with respect to standard CNNs. First of all a DM‐CNN computes during the time evolution of the memristor fluxes, instead of the capacitor voltages as for a standard CNN. Furthermore, when a steady state is reached, the memristors keep in memory the result of the computation, that is the limiting values of the fluxes, while all memristor currents and voltages, as well as all currents, voltages, and power in the DM‐CNN vanish. Instead, for standard CNNs, currents, voltages, and power do not drop off when a steady state is reached. Copyright © 2016 John Wiley & Sons, Ltd.     

基于卷积神经网络的暂态电压稳定评估及风险量化

提出了一种暂态电压稳定性评估及其风险量化方法.首先,探讨卷积神经网络(CNN)与暂态电压稳定评估的关联性和匹配性,建立了基于CNN的暂态电压稳定评估模型.其次,在可信度框架下引入四元评估结构,可有效解决CNN在稳定边界识别上对时域仿真依赖的难题.然后,通过评估结果获取暂态电压稳定裕度,并将其与可信度相结合来构建风险函数...     

Cellular neural network with trapezoidal activation function

This paper presents a cellular neural network (CNN) scheme employing a new non‐linear activation function, called trapezoidal activation function (TAF). The new CNN structure can classify linearly non‐separable data points and realize Boolean operations (including eXclusive OR) by using only a single‐layer CNN. In order to simplify the stability analysis, a feedback matrix W is defined as a function of the feedback template A and 2D equations are converted to 1D equations. The stability conditions of CNN with TAF are investigated and a sufficient condition for the existence of a unique equilibrium and global asymptotic stability is derived. By processing several examples of synthetic images, the analytically derived stability condition is also confirmed. Copyright © 2005 John Wiley & Sons, Ltd.     

APPLICATION OF FUZZY CELLULAR NEURAL NETWORK TO MORPHOLOGICAL GREY-SCALE RECONSTRUCTION

The fuzzy cellular neural network (FCNN) is a brand new branch of cellular neural network (CNN). In this paper, an additive structure of FCNN is proposed for the purpose of implementation of grey-scale mathematical morphology operators. Then this additive FCNN is used to implement morphological grey-scale reconstruction. Some applications of the FCNN to image processing are proposed based on morphological grey-scale reconstruction. Computer simulation results are given. © 1997 by John-Wiley & Sons, Ltd.     

Generation of n-double scrolls via cellular neural networks

N-double scrolls are chaotic attractors generated by Chua's circuit when its non-linear resistor characteristic is appropriately modified. They have recently been introduced, simulated and studied analytically by Suykens and Vandewalle. In this paper a new approach to generate n-double scroll attractors is introduced. They have been derived from a connection of three simple generalized cellular neural network cells called a state controlled CNN (SC-CNN). In this way it is established that n-double scroll attractors can be generated using an SC-CNN. The circuit implementation of the introduced system and some experimental results referring to the 2-double scroll attractor are reported.     

Cellular neural networks: Theory and circuit design

Cellular neural networks or CNNs are a novel neural network architecture introduced by Chua and Yang which is very general and flexible, has some important properties desirable for design applications and can be efficiently implemented on custom hardware based on analogue VLSI technology. In this paper an abstract normalized definition of cellular neural networks with arbitrary interconnection topology is given. Instead of stability, the property of convergence is found to be of central importance: large classes of convergent CNNs in practice always asymptotically approach some stable equilibrium where each component of the corresponding output is binary-valued. A highly efficient CMOS-compatible CNN circuit architecture is then presented where a basic cell consists of only two fully differential op amps, two capacitors and several MOSFETs, while a variable interconnection weight is realized with only four MOSFETs. Since all these elements are standard components in the current analogue IC technology and since all network functions are implemented directly on the device level, this architecture promises high cell and interconnection densities and extremely high operating speeds.     

基于改进卷积神经网络的电网假数据注入攻击检测方法

假数据注入攻击可以篡改由数据采集与监控(SCADA)系统采集到的量测信息,影响电网的重要决策,从而对电网状态估计造成安全威胁。针对智能电网状态估计,研究了交流模型下假数据注入攻击的原理,构建了基于改进卷积神经网络(CNN)的假数据注入攻击检测模型。将门控循环单元(GRU)结构加入CNN中的全连接层之前构建CNN-GRU混合神经网络,根据电网历史量测数据进行训练并更新网络参数,提取数据的空间和时间特征,并根据提出的模型设计实现了高效实时的假数据注入攻击检测器。最后,在IEEE 14节点和IEEE 118节点测试系统中,与基于传统CNN、循环神经网络及深度信念网络的检测方法分别进行了大量对比实验,验证了所提方法的有效性。     

Blind signal separation using cellular neural networks

In this paper a two-layer cellular neural network (CNN) is used to separate blind signals. the topological structures of the CNN and the inner parameters are presented. the first CNN layer functions as an adaptive filter which converges asymptotically to an equilibrium point in the mean. A stochastic stability model is used to find conditions under which cells in the first layer converge. Conditions leading to correct equilibrium solutions are also presented using this model. the second CNN layer functions as a signal separator. Simulations show that the CNN blind signal separator has strong robustness and works even better than the theory predicts.     

利用多空间尺度下时空相关性的点云分布多风机风速预测

在风机呈不规则排列的风电场中,不同空间位置下的众多风机分布构成点云,而不是规则化的矩形网格.点云是不规则且无序的,可以代表任意风电场中多风机的地理位置分布,但是不能构成卷积神经网络(CNN)高度规则的网格输入,卷积算子难以学习其空间局部相关性.若直接将不规则点云映射为网格排列进行常规卷积,会失去点云原始的空间信息.为此,采用点CNN进行空间相关性提取,再利用简单循环单元进行时间相关性提取,从而获取点云数据的时空相关性.同时,设计点CNN时融入了多尺度下的空间特征提取与汇集.最后,结合实际以点云分布的多风机仿真结果验证了所提预测模型的有效性.