Design of high-speed,high-density CNNS in cmos technology

An analogue VLSI circuit architecture for the CMOS implementation of cellular neural networks (CNNs) is presented. It is based exclusively on the use of small capacitors and operational transconductance amplifiers operating in continuous time. Integrated circuit implementations of this architecture are very well suited for processing applications requiring large array size and high speed. We describe a systematic design approach for those circuits and present the design, fabrication and testing of two chips. These chips are used for connected component detection applications and are the first working integrated circuit implementation of a CNN. They contain 2000 transistors and have been fabricated using 2 μm CMOS technology. the density is 32 cells per square millimetre of silicon and the time constant of the processing is of the order of 10^?7 s. Experimental results of static and dynamic tests are given, including a complete image-processing example.     

Stability analysis of generalized cellular neural networks

A rather general class of neural networks, called generalized cellular neural networks (CNNs), is introduced. the new model covers most of the known neural network architectures, including cellular neural networks, Hopfield networks and multilayer perceptrons. Several sets of conditions ensuring the input-output stability and global asymptotic stability of generalized CNNs have been obtained. the conditions for the stability of individual cells are checked in the frequency domain, while the stability of the overall network is analysed in terms of the stability of individual cells and the connectivity characteristics. the results on the global asymptotic stability are useful for the design of a generalized CNN such that the orbit of each state converges to a globally asymptotically stable equilibrium point which depends only on the input and not on the initial state. Such a network defines an algebraic map from the space of external inputs to the space of steady state values of the outputs and hence can accomplish cognitive and computational tasks.     

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.     

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.     

External inputs,stable equilibria and complete stability of CNNs

We construct two cellular neural networks (CNNs) of three cells to show that a CNN can have stable equilibria, but is not completely stable and that the complete stability of CNN depends on the choice of external inputs. These phenomena cannot occur for two‐cell CNNs. Copyright © 2003 John Wiley & Sons, Ltd.     

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.     

Neural networks for constrained optimization problems

This paper is concerned with utilizing neural networks and analogue circuits to solve constrained optimization problems. We propose a novel neural network architecture for solving a class of non-linear programming problems. the proposed neural network is then used, and if necessary modified, to solve minimum norm problems subject to linear constraints. Minimum norm problems have many applications in various areas, but we focus on their applications to the control of discrete dynamic processes. the applicability of the proposed neural network is demonstrated on numerical examples.     

Energy Efficient System-on-chip Design for Wireless Body Area Sensor Network

This article presents a novel energy-efficient on-chip design for wireless body area sensor networks focused towards pervasive healthcare applications. The network adopts a master–slave architecture, where the body-worn slave nodes periodically send sensor readings to a central master node. Unlike traditional peer-to-peer wireless sensor networks, the nodes in this wireless body area sensor networks are not deployed in an ad hoc fashion. The network is centrally managed and all communications are single-hop. A cluster algorithm is also presented so that all slave nodes are within the transmission range of the master nodes. It pretends that all slave nodes can share resources and information over the internet to reduce energy consumption. The design on system-on-chip platform has been simulated for some experiments and implemented. Compared to published and industrially used schemes, the power consumption of the proposed design is over 30 and 99% lower in the simulation and platform implementation, respectively.     

Robot vision with cellular neural networks: a practical implementation of new algorithms

Cellular neural networks (CNNs) are well suited for image processing due to the possibility of a parallel computation. In this paper, we present two algorithms for tracking and obstacle avoidance using CNNs. Furthermore, we show the implementation of an autonomous robot guided using only real‐time visual feedback; the image processing is performed entirely by a CNN system embedded in a digital signal processor (DSP). We successfully tested the two algorithms on this robot. Copyright © 2006 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 digital multiprocessor hardware accelerator board for cellular neural networks: CNN-HAC

Analogue realizations of neural networks are superior in speed. the hardware accelerator boards using catalogue programmable VLSI ICs represent a trade-off having higher reconfigurability and lower cost. This paper presents such a solution for a cellular neural network (CNN). The architecture of the present design (CNN-HAC) using four standard DSPs to calculate the transient response of a one-layer CNN containing (0.25–0.75) × 10⁶ analogue neural cells (depending on the type of template) is presented. the architecture and also the design principles are independent of the number of processors. the actual design was made in the form of a PC add-on board. The global control unit, which connects the board to the host firmware and communicates control signals to/from the local control units of the DSPs, was realized mainly with EPLDs. A special correspondence between the virtual processing elements—calculating the time-discrete models of the analogue neural cells—and the physical ones is discussed in detail. It is realized in an architecture with a simple, two-directional interprocessor communication. This architecture can be ‘scaled down’ using faster processors, EPLDs and memories. the present version runs with 2 μs/cell/iteration speed.     

A study on WTA cellular neural networks

This paper addresses a number of basic issues concerning the dynamics of a class of winner‐take‐all cellular neural networks (WTA CNNs) proposed by Seiler and Nossek. The main result is an analytical estimate for the settling time, which shows from a theoretical point of view that such CNNs are well suited for on‐line applications requiring a large number of units, fast processing speed and a relatively high resolution. Other results are the determination of the largest parameter set that guarantee a correct WTA functionality for all initial conditions and the solution of a conjecture made by Seiler and Nossek. These results are proved by means of a new Lyapunov function to analyse the global dynamical behaviour of the WTA CNNs. Copyright © 2001 John Wiley & Sons, Ltd.     

Membrain: A cellular neural network model based on a vibrating membrane

This paper introduces the Membrain model describing a neural network architecture which is similar to the architecture underlying the class of cellular neural networks (CNNs). the main difference pertains to the characteristic processing equation, which is based on a wave equation instead of a heat equation. Within the CNN framework, a cellular Membrain model may be obtained by replacing the neuron output function by a first-order state equation. Furthermore, the network-cloning templates are chosen such that the CNN behaves like a system of coupled harmonical oscillators. Since the energy of such a system is bounded, the piecewise linear neuron characteristic function may be chosen such that it always operates in the linear regime. Our starting point is the analytical and general solution for forced vibrations with damping. This solution applies to a Membrain neural network whose functional architecture is based on the specialized solution for a network of coupled harmonic oscillators. In particular, we present a Membrain CNN (MCNN) having a toroidal connection structure such that the natural modes of vibration of the net are translation-invariant. Moreover, depending on the point group of the network, some rotation invariance can also be obtained. Identifying the input of such a network with the initial state of the oscillators gives rise to an output which is in essence a transversally travelling wave made up of components which are coupled harmonic neuronal oscillators; that is, the wave is a superposition of natural modes of vibration of the network. the temporal wave pattern may be transformed into a one-dimensional temporal signal which is invariant under translation of the initial deflection pattern of the MCNN. the amplitudes of the components in the temporal signal correspond to the power spectrum of the natural vibration modes in the MCNN. Interpreting the initial deflection pattern as a grey-level image, the temporal signal can be viewed as a modulation of a translation-invariant ‘fingerprint’ of the image. the signal may be sampled such that the modulated ‘fingerprint’ can be classified using some of the traditional neural network models. In particular we show that (1) a self-organizing feature map clusters correlated images and (2) a back-propagation neural network extracts position-invariant features.     

Frequency domain criteria for cellular neural networks

A dynamical system is called globally asymptotically stable if it has a unique equilibrium point which attracts every trajectory in state space. As a consequence its steady state response is insensitive to initial conditions and then depends only on the input. In this paper some criteria are presented for the global asymptotic stability of cellular neural networks (CNNs), concerning both discrete-time and continuous-time dynamics. The proposed criteria represent necessary and sufficient conditions that can easily be checked by computing the discrete Fourier transform of the template elements. For this reason they have been called frequency domain stability criteria. These criteria provide milder constraints on the template coefficients than required in existing results for general recurrent neural network models. © 1997 by John Wiley & Sons, Ltd.     

Long transient oscillations in a class of cooperative cellular neural networks

The real‐time processing capabilities of cellular neural networks (CNNs) are inherently related to the fast convergence time of the solutions toward the asymptotically stable equilibrium points. A typical requirement is that the settling time should not exceed a few (or at most 10) cell time constants. This paper introduces a class of completely stable nonsymmetric cooperative CNN rings whose solutions display unexpectedly long transient oscillations for a wide set of initial conditions and for a wide set of interconnection parameters. Numerical simulations show that the oscillations can easily last hundreds of cycles, and thousands of cell time constants, before settling to a steady state, thus possibly impairing their real‐time processing capabilities. Goal of the paper is also to show, by means of laboratory experiments on a discrete component prototype of the CNN ring, that the long oscillation phenomenon is physically robust with respect to the non‐idealities of the circuit implementation. The experiments show some other peculiar features of the long lasting oscillations as the metamorphosis between different periodic behaviors during the transient. Finally, analytic asymptotic estimates on the duration of the transient oscillations are provided. Copyright © 2013 John Wiley & Sons, Ltd.     

华北电网与山东电网联网论证

通过对华东与山东联网方案和性质、规模的分析,论证其可行性。华东电网与山东电网联网主要可得到互为备用效益及季错峰效益,并对提高电网的运行稳定性有一定作用。     

基于嵌入式Internet的变电站智能设备接入技术研究

以嵌入式Internet技术为基础,研究变电站内的智能电子设备(IED)如何实现嵌入式Internet接入．从而实现现场生产信息在广域范围内的共享。讨论了变电站IED嵌入式Internet的接入模式．详细阐述了实现IED嵌入式Internet接入的关键技术,提出采用“胖”客户机“瘦”服务器的系统体系结构满足硬件资源的限制。Web页面集成嵌入式Java Applet解决数据动态更新问题。裁剪TCP／IP协议并利用HTTP1．1协议持续连接和缓存控制特性解决实时性问题。最后．对变电站内的IED嵌入式Internet接入后引起的网络安全问题及应采取的安全策略进行了探讨。     

No wiring constraints

Wireless technologies are gaining increased acceptance as a viable technology within the industrial solution space. However, it is important for control system architects to fully understand not only the benefits but also the challenges and limitations of wireless technologies. The complex interrelationships between the various wireless technology characteristics require careful tradeoffs to be made in the design of the solution. This is especially true for applications that need to handle continuous data streams where high capacity is critical to maintain the scalability and reliability of the network. Although still relatively new, mesh networks have proven themselves to have significant advantages in robustness, scalability, and low-power consumption over other wireless technologies for use in industrial monitoring applications. Mesh networks employing PDR address the scalability challenges of wireless sensor networking that can limit implementation in production situations by significantly increasing overall packet-delivery rate and reducing network communication overhead. HC_WSN, an approach that will emerge as the important next trend in the wireless sensor networking industry, can be implemented in addition to PDR techniques and will further support the increasing number of applications that require the wireless sensor network to handle greater amounts of data.     

基于多神经网络融合的短期负荷预测方法

为了利用不同深度神经网络的优势,提高深度学习算法对短期负荷的预测能力,提出一种基于多神经网络融合的短期负荷预测方法。以电力系统历史有功负荷、季节、日期类型和气象数据为输入特征,并行架构的深度神经网络和注意力机制网络为核心网络;以并行架构中的卷积神经网络通道提取静态特征,门控循环单元网络通道挖掘动态时序特征,采用注意力机制网络融合提取的特征并动态调整网络对不同特征的依赖程度;使用Maxout网络增强网络整体的非线性映射能力,通过全连接网络输出预测结果。与支持向量机、长短期记忆网络的算例结果对比表明,所提方法具有更高的预测平稳性和准确性。     

基于多层前向神经网络的伺服系统逆动态控制

建立了非线性伺服系统逆动态控制的体系结构,利用启发式模拟退火算法,进行了基于神经网络方法对象模型未知条件下的伺服系统逆动态控制仿真研究。