Stability analysis of delayed cellular neural networks described using cloning templates

We show that any N/spl times/M-dimensional delayed cellular neural network described using cloning templates can have no more than 3/sup N/spl times/M/ isolated equilibrium points and 2/sup N/spl times/M/ of these equilibrium points located in saturation regions are locally exponentially stable. In addition, we give the conditions for the equilibrium points to be locally exponentially stable when the equilibrium points locate the designated saturation region. These conditions improve and extend the existing stability results in the literature. The conditions are also very easy to be verified and can be checked by direct examination of the templates, regardless of the number of cells. Finally, the validity and performance of the results are illustrated by use of two numerical examples.     

Signal processing using cellular neural networks

The cellular neural network (CNN) architecture combines the best features from traditional fully-connected analog neural networks and digital cellular automata. The network can rapidly process continuous-valued (gray-scale) input signals (such as images) and perform many computation functions which traditionally were implemented in digital form. Here, we briefly introduce the the theory of CNN circuits, provide some examples of CNN applications to image processing, and discuss work toward a CNN implementation in custom CMOS VLSI. The role of analog computer-aided design (CAD) will be briefly presented as it relates to analog neural network implementation.This work is supported in part by the Office of Naval Research under Contract N00014-89-J1402, and the National Science Foundation under grant MIP-8912639.     

Stability of cellular neural networks with delay

The problem of stability for cellular neural networks with delay (DCNNs) is studied. A new sufficient condition related to the global asymptotic stability for DCNNs is derived. This condition is less restrictive than that given in the literature     

具有时滞的细胞神经网络的稳定性

该文研究了具有时滞的细胞神经网络的稳定性问题,运用Lyapunov泛函法和Razumikhin法分别给出了时滞细胞神经网络全局渐近稳定的两个新的充分条件。其中,第一个条件与时延无关,而第二个条件与时延有关。获得的定理推广了已有文献中的结果,对于时滞细胞神经网络的硬件设计具有一定的指导意义。     

Nonlinear digital filters mimicking cellular neural networks

This work is motivated by the need forFaithful digital simulation of cellular neural networks (CNNs) that maintains most of their qualitative properties of stability and convergence. An interconnection of nonlinear digital filters mimicking behaviors of the analog CNNs is proposed, and the main properties are studied in detail. The discrete model obtained is proven to have the same convergence properties as the original analog network. The key to this development is the use of anAppropriate discretization scheme. Our discrete approximation to the nonlinear state-space representation of cellular neural networks is such that the Lyapunov function used to show convergence in analog cellular neural networks is still a Lyapunov function (when appropriately discretized) for our nonlinear digital filter network. This is in contrast to other digital simulations of CNNs, which have not been proven to preserve the convergence properties. The network of nonlinear digital filters so introduced thus adds another item to the catalog of digital filters obtained viaappropriate discretization of analog circuits, e.g., wave digital filters, orthogonal filters, and certain other of their more recently studied nonlinear counterparts.All authors were with the Stevens Institute of Technology, Hoboken, NJ, 07670 when this work was performed.Support from NSF grant MIP 9696176.     

Discrete-time cellular neural networks using distributed arithmetic

An efficient digital architecture for the discrete-time cellular neural networks (DTCNNs) is proposed that employs the distributed arithmetic (DA). It consumes little silicon area because of the bit serial computation of DA, and offers higher speed operation than the analogue implementations of DTCNN. The proposed architecture has been implemented in a 0.8 μm CMOS technology     

一种基于量子细胞自动机的三维量子细胞神经网络

以量子细胞自动机为神经元,提出了一种三维的量子细胞神经网络结构;该量子细胞网络包含上下两层的量子细胞自动机阵列,并引入了A模板、B模板以及阈值的概念.以量子细胞自动机的极化率为像素值,通过选择不同的模板、阈值等参数.使得量子细胞神经网络实现了"与"、"或"、"非"操作以及边缘提取等图像处理功能,并利用MATLAB进行了仿真验证,数值仿真结果验证了其在图像处理上的有效性.与传统的细胞神经网络相比,量子细胞神经网络易于实现超大规模且具有超低功耗、超高集成度等优点.     

Performance analysis of cellular networks

Dynamic channel allocation can reduce the probability of blocking in cellular telephone networks. However, more is needed to achieve optimal performance. The author aims at estimating the minimal blocking probability for some simple cellular networks. Some dynamic channel allocation strategies are analyzed, the optimal performance (obtained by dynamic allocation and flow control) of some very simple networks is computed, and simple bounds on optimal performance are presented. These results lead to a better understanding of cellular networks and can be used to evaluate new control algorithms     

Smart-pixel cellular neural networks in analog current-mode CMOStechnology

This paper presents a systematic approach to design CMOS chips with concurrent picture acquisition and processing capabilities. These chips consist of regular arrangements of elementary units, called smart pixels. Light detection is made with vertical CMOS-BJT's connected in a Darlington structure. Pixel smartness is achieved by exploiting the cellular neural network paradigm, incorporating at each pixel location an analog computing cell which interacts with those of nearby pixels. We propose a current-mode implementation technique and give measurements from two 16 x 16 prototypes in a single-poly double-metal CMOS n-well 1.6-μm technology. In addition to the sensory and processing circuitry, both chips incorporate light-adaptation circuitry for automatic contrast adjustment. They obtain smart-pixel densities up to 89 units/mm², with a power consumption down to 105 μW/unit and image processing times below 2 μs     

Complete stability of cellular neural networks with time-varying delays

In this paper, the complete stability of cellular neural networks with time-varying delays is analyzed using the induction method and the contraction mapping principle. Delay-dependent and delay-independent conditions are obtained for locally stable equilibrium points to be located anywhere, which differ from the existing results on complete stability where the existence of equilibrium points in the saturation region is necessary for complete stability and locally stable equilibrium points can be in the saturation region only. In addition, some existing stability results in the literature are special cases of a new result herein. Simulation results are also discussed by use of two illustrative examples.     

Invariance principle and complete stability for cellular neural networks

In applications of classification of patterns, image processing, associative memories etc, the complete stability of cellular neural networks (CNNs) plays an important role. Invariance principles based on the Lyapunov functions and functionals are still the most advantageous theory to analyze the complete stability. However, one difficulty in applying classical invariance principles to the complete stability is to prove that the largest invariant set consists of equilibrium points. In this paper, we present one invariance principle to analyze the complete stability. We can avoid the difficulty of proving that the largest invariant set is constituted of equilibrium points in discussing some sufficient condition for complete stability of CNNs by using this invariance principle.     

LOGICAL ANALYSIS FOR NEURAL NETWORKS

By comparison with constraint satisfaction networks, this paper presents an essential frame of the logical theory for continuous-state neural networks, and gives the quantitative analyzing method for contradiction. The analysis indicates that the basic reason for the alternation of the logical states of the neurons is the existence of superior contradiction inside the networks. The dynamic process for a neural network to find a solution corresponds to eliminating the superior contradiction.     

New results for exponential stability of delayed cellular neural networks

This brief presents new sufficient conditions for the global exponential stability of the equilibrium point for delayed cellular neural networks (DCNNs). It is shown that the use of a more general type of Lyapunov-Krasovskii functional enables us to derive new results for 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.     

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.     

Teletraffic analysis of hierarchical cellular communication networks

The sustained increase of users and the request for advanced multimedia services are amongst the key motivations for designing new high-capacity cellular telecommunication systems. The proposals that are being pursued by several studies and field implementations consider hierarchical architectures and dynamic resource allocation. A hierarchical cellular communication network is analyzed, taking user mobility into account and exploiting dynamic channel-allocation schemes. In particular, a finite number of users has been considered, moving at different speeds in a geographical region covered by a finite number of cells structured in two hierarchical levels: micro- and macrocells. For such a system, mobility and traffic models have been developed, both based on queueing networks analyzing maximum packing (MP), a dynamic channel-allocation scheme. The obtained results, validated by simulation experiments, allow the evaluation of main system-performance parameters in terms of new-call and handoff blocking probabilities, and forced-termination probability as a function of load and system parameters.     

Novel global asymptotic stability criteria for delayed cellular neural networks

This brief provides improved conditions for the existence of a unique equilibrium point and its global asymptotic stability of cellular neural networks with time delay. Both delay-dependent and delay-independent conditions are obtained by using more general Lyapunov-Krasovskii functionals. These conditions are expressed in terms of linear matrix inequalities, which can be checked easily by recently developed standard algorithms. Examples are provided to demonstrate the reduced conservatism of the proposed criteria by numerically comparing with those reported recently in the literature.     

Satellite image analysis using neural networks

The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, Satellite Image Analysis using Neural Networks (SIANN), that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four-step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed has been completed and applied to climatological data.     

Conditions of asymptotic stability for cellular neural networks with time delay

In this paper, global asymptotic stability for cellular neural networks with time delay is discussed using a novel Liapunov function. Some novel sufficient conditions for global asymptotic stability are obtained. Those results are simple and practical than those given by P. P. Civalleri, et al., and have a leading importance to design cellular neural networks with time delay.     

A new method of Lyapunov functionals for delayed cellular neural networks

Lyapunov functionals and Lyapunov functions coupled with the Razumikhin technique are still the most popular tools in studying the stability of large-scale retarded nonlinear systems. However, it is generally difficult to construct Lyapunov functionals or functions that satisfy the strong conditions required in the classical stability theory. We show that for some delay differential systems such as additive neural networks with delays, we can weaken the condition that the Lyapunov functional or function is positive definite, by using the equivalence between the state stability and the output stability. We apply our general theory to obtain some new stability conditions for cellular neural network models. It is represented that it is easy to construct Lyapunov functionals or functions satisfied conditions of our theorems.