Equilibrium analysis of cellular neural networks

Cellular neural networks are dynamical systems, described by a large set of coupled nonlinear differential equations. The equilibrium point analysis is an important step for understanding the global dynamics and for providing design rules. We yield a set of sufficient conditions (and a simple algorithm for checking them) ensuring the existence of at least one stable equilibrium point. Such conditions give rise to simple constraints, that extend the class of CNN, for which the existence of a stable equilibrium point is rigorously proved. In addition, they are suitable for design and easy to check, because they are directly expressed in term of the template elements

Moving Object Detection Using Delayed - Cellular Neural Network

Bu makalede, 2 boyutlu görüntüler geciktirilmiş hücresel yapay sinir ağı (GHYSA) ile incelenmiştir. GHYSA ilk defa 1993 yılında tanıtılmıştır. Gecikme miktarı belirli bir değerden küçük seçilmesi halinde, asimptotik kararlılık sağlanır. Özellikle son yıllarda, görüntünün hareketli olan kısmı diğer bölgelere göre daha önemli olabilmektedir. Tıp biliminde, kanserli hücrelerin yönelimini GHYSA ile belirlemek çok önem taşımaktadır. Burada Java dilinde yazılım gerçekleştirilmiş ve yapay örnekler için iyi sonuçlar elde edilmiştir.In this paper, we have studied moving objects in 2-D images using Delayed Cellular Neural Network (DCNN). DCNN was first introduced in 1993. It is shown that for a network whose cells are specified, complete asymptotic stability providing the delay is less than a bound which depends on only the cell parameters. Especially nowadays, only moving part of the whole image is getting more important according to the practical cases such as estimation of biomedical issues which is enlarging due to the cancer property. We have used Java language for our synthetic examples and satisfactory results were obtained

Moving Object Detection Using Delayed - Cellular Neural Network

Bu makalede, 2 boyutlu görüntüler geciktirilmiş hücresel yapay sinir ağı (GHYSA) ile incelenmiştir. GHYSA ilk defa 1993 yılında tanıtılmıştır. Gecikme miktarı belirli bir değerden küçük seçilmesi halinde, asimptotik kararlılık sağlanır. Özellikle son yıllarda, görüntünün hareketli olan kısmı diğer bölgelere göre daha önemli olabilmektedir. Tıp biliminde, kanserli hücrelerin yönelimini GHYSA ile belirlemek çok önem taşımaktadır. Burada Java dilinde yazılım gerçekleştirilmiş ve yapay örnekler için iyi sonuçlar elde edilmiştir.In this paper, we have studied moving objects in 2-D images using Delayed Cellular Neural Network (DCNN). DCNN was first introduced in 1993. It is shown that for a network whose cells are specified, complete asymptotic stability providing the delay is less than a bound which depends on only the cell parameters. Especially nowadays, only moving part of the whole image is getting more important according to the practical cases such as estimation of biomedical issues which is enlarging due to the cancer property. We have used Java language for our synthetic examples and satisfactory results were obtained

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. © 2005 IEEE.published_or_final_versio

Biquadratic transconductance switched capacitor filters

Cataloged from PDF version of article.Switched-capacitor (SC) filters yield efficient implementations in integrated form. However, they employ op-amps, each of which must be designed for a given filter separately. Furthermore, SC filters are not tunable. This work presents a new type of sampled data filters consisting of transconductance elements, switches and capacitors, called transconductor switched capacitor (TSC) filters. Transconductance elements do not degrade their performance within a wide frequency range and tunable ones are available

Delay-Induced Transient Oscillations in a Two-Neuron Network

Finite transmission times between neurons, referred to as delays, may appear in hardware implementation of neural networks. We analyze the dynamics of a two-neuron network in which the delay modifies the transient and not the long-term behavior of the network. We show that the delay causes some trajectories to oscillate transiently before reaching stationary behavior and the duration of these transients increases exponentially with the delay. Such a phenomeno deteriorates network performance

Global exponential stability of a class of neural networks with unbounded delays

In this paper, the global exponential stability of a class of neural networks is investigated. The neural networks contain variable and unbounded delays. By constructing a suitable Lyapunov function and using the technique of matrix analysis, some new sufficient conditions on the global exponential stability are obtained.Досліджено глобальну експоненціальну стійкість одного класу нейронних сіток. Нейронні сітки містять змінні та необмежені загаювання. На основі побудови відповідної функції Ляпунова та техніки матричного аналізу отримано нові достатні умови глобальної експоненціальної стійкості

On the Exponential Stability and Periodic Solutions of Delayed Cellular Neural Networks

AbstractA set of criteria is presented for the global exponential stability and the existence of periodic solutions of delayed cellular neural networks (DCNNs) by constructing suitable Lyapunov functionals, introducing many parameters and combining with the elementary inequality technique. These criteria have important leading significance in the design and applications of globally stable DCNNs and periodic oscillatory DCNNs. In addition, earlier results are extended and improved; other results are contained. Two examples are given to illustrate the theory

Finite-time synchronization of Markovian neural networks with proportional delays and discontinuous activations

In this paper, finite-time synchronization of neural networks (NNs) with discontinuous activation functions (DAFs), Markovian switching, and proportional delays is studied in the framework of Filippov solution. Since proportional delay is unbounded and different from infinite-time distributed delay and classical finite-time analytical techniques are not applicable anymore, new 1-norm analytical techniques are developed. Controllers with and without the sign function are designed to overcome the effects of the uncertainties induced by Filippov solutions and further synchronize the considered NNs in a finite time. By designing new Lyapunov functionals and using M-matrix method, sufficient conditions are derived to guarantee that the considered NNs realize synchronization in a settling time without introducing any free parameters. It is shown that, though the proportional delay can be unbounded, complete synchronization can still be realized, and the settling time can be explicitly estimated. Moreover, it is discovered that controllers with sign function can reduce the control gains, while controllers without the sign function can overcome chattering phenomenon. Finally, numerical simulations are given to show the effectiveness of theoretical results