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1 edition of Cellular Neural Networks found in the catalog.

Cellular Neural Networks

Analysis, Design and Optimization

by Martin Hänggi

  • 237 Want to read
  • 23 Currently reading

Published by Springer US in Boston, MA .
Written in English

    Subjects:
  • Systems engineering,
  • Engineering,
  • Computer-aided design,
  • Computer engineering

  • About the Edition

    Cellular Neural Networks (CNNs) constitute a class of nonlinear, recurrent and locally coupled arrays of identical dynamical cells that operate in parallel. ANALOG chips are being developed for use in applications where sophisticated signal processing at low power consumption is required. Signal processing via CNNs only becomes efficient if the network is implemented in analog hardware. In view of the physical limitations that analog implementations entail, robust operation of a CNN chip with respect to parameter variations has to be insured. By far not all mathematically possible CNN tasks can be carried out reliably on an analog chip; some of them are inherently too sensitive. This book defines a robustness measure to quantify the degree of robustness and proposes an exact and direct analytical design method for the synthesis of optimally robust network parameters. The method is based on a design centering technique which is generally applicable where linear constraints have to be satisfied in an optimum way. Processing speed is always crucial when discussing signal-processing devices. In the case of the CNN, it is shown that the setting time can be specified in closed analytical expressions, which permits, on the one hand, parameter optimization with respect to speed and, on the other hand, efficient numerical integration of CNNs. Interdependence between robustness and speed issues are also addressed. Another goal pursued is the unification of the theory of continuous-time and discrete-time systems. By means of a delta-operator approach, it is proven that the same network parameters can be used for both of these classes, even if their nonlinear output functions differ. More complex CNN optimization problems that cannot be solved analytically necessitate resorting to numerical methods. Among these, stochastic optimization techniques such as genetic algorithms prove their usefulness, for example in image classification problems. Since the inception of the CNN, the problem of finding the network parameters for a desired task has been regarded as a learning or training problem, and computationally expensive methods derived from standard neural networks have been applied. Furthermore, numerous useful parameter sets have been derived by intuition. In this book, a direct and exact analytical design method for the network parameters is presented. The approach yields solutions which are optimum with respect to robustness, an aspect which is crucial for successful implementation of the analog CNN hardware that has often been neglected. `This beautifully rounded work provides many interesting and useful results, for both CNN theorists and circuit designers." Leon O. Chua.

    Edition Notes

    Statementedited by Martin Hänggi, George S. Moschytz
    ContributionsMoschytz, George S.
    Classifications
    LC ClassificationsTK7888.4
    The Physical Object
    Format[electronic resource] :
    Pagination1 online resource (xi, 148 p.)
    Number of Pages148
    ID Numbers
    Open LibraryOL27023515M
    ISBN 101441949887, 1475732201
    ISBN 109781441949882, 9781475732207
    OCLC/WorldCa851794277

    Chapter 2 Cellular Neural Network Introduction The cellular neural network (abbreviated as CNN) is proposed by Chua and Yang in [1]-[2]. It is more general than Hopfield neural network. The state value of one node (cell) at the next time is influenced by inputs and outputs of nodes near this. What is Cellular Neural Networks? Definition of Cellular Neural Networks: A particular circuit architecture which possesses some key features of Artificial Neural Networks. Its processing units are arranged in an M×N grid. The basic unit of Cellular Neural Networks is called cell and contains linear and non linear circuit elements. Each cell is connected only to its neighbour cells.

    Basic Cellular Neural Networks Image Processing: /ch Since its seminal publication in , the Cellular Neural Network (CNN) (Chua & Yang, ) paradigm have attracted research community’s attention, mainlyAuthor: J. Álvaro Fernández. IEEE COMMUNICATIONS SURVEYS & TUTORIALS 1 Deep Learning in Mobile and Wireless Networking: A Survey Chaoyun Zhang, Paul Patras, and Hamed Haddadi Abstract—The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprece-dented demands on mobile and wireless networking Size: 8MB.

    This book explores how neural networks can be designed to analyze sensory data in a way that mimics natural systems. It introduces readers to the cellular neural network (CNN) and formulates it to match the behavior of the Wilson–Cowan model. A novel class of information-processing systems called cellular neural networks is proposed. Like neural networks, they are large-scale nonlinear analog circuits that process signals in real time.


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Cellular Neural Networks by Martin Hänggi Download PDF EPUB FB2

This book presents a comprehensive description of the emerging technology of cellular neural networks (CNNs), the first general purpose analog microprocessors with applications including real-time image and audio processing, image recognition, and the solution of partial differential : $ From the Publisher The definitive reference for the world of Cellular Neural Networks (CNN).

Demonstrates basic notions and possibilities using examples which include global optimization in a single transient, color halftoning and a CNN algorithm containing several cloning templates for detecting almost invisible defects in a textile by: Cellular Nonlinear/Neural Network (CNN) technology is both a revolutionary concept and an experimentally proven new computing paradigm.

Analogic cellular computers based on CNNs are set to change the way analog signals are by: Cellular Neural Networks Cellular Neural Networks book were introduced in by L O Chua and L Yang as a novel class of information processing systems, which possesses some of the key features of Neural Networks and which has important potential applications in such areas as image processing and pattern : $ Cellular Neural Networks (CNNs) constitute a class of nonlinear, recurrent and locally coupled arrays of Cellular Neural Networks book dynamical cells that operate in parallel.

ANALOG chips are being developed for use in applications where sophisticated signal processing at low power consumption is : Hardcover. Cellular Neural Networks: Dynamics And Modelling (Mathematical Modelling: Theory And Applications): Medicine & Health Science Books @ ed by: One way of looking at neural networks is to consider them to be arrays of nonlinear dynamical systems that interact with each other.

This book deals with one class of locally coupled neural net­ works, called Cellular Neural Networks (CNNs). Introduction This book presents a comprehensive description of the emerging technology of cellular neural networks (CNNs), the first general purpose analog microprocessors with applications including real-time image and audio processing, image recognition, and the solution of partial differential equations.

Cellular Neural Networks (CNNs) constitute a class of nonlinear, recurrent and locally coupled arrays of identical dynamical cells that operate in parallel. ANALOG chips are being developed for use in applications where sophisticated signal processing at low power consumption is required.

A vision researcher, engineer, or computer scientist seeking to understand cellular neural networks should start with this book. I do take issue, however, with the authors' statements that "no electronic circuit knowledge is needed to und erstand the first 14 chapters of this book," and that only modest knowledge of mathematics and physics is.

Cellular Nonlinear/neural Network (CNN) technology is both a revolutionary concept and an experimentally proven new computing paradigm. Analogic cellular computers based on CNNs are set to change the way analog signals are processed and are paving the way to an analog computing : Leon O.

Chua, Tamas Roska. Title: Cellular neural networks: theory - Circuits and Systems, IEEE Transactio ns on Author: IEEE Created Date: 2/26/ AM. Definitions. A Cellular Neural Network is a system of cells (or neurons) defined on a normed space (cell grid), which is a discrete subset of ℜn(generally n≤3), with distance function d.

Abstract: The theory of a novel class of information-processing systems, called cellular neural networks, which are capable of high-speed parallel signal processing, was presented in a previous paper (see ibid., vol, no, p, ).Cited by: Many results on the algorithm development, VLSI implementations of CNN systems are reported in the first three IEEE International Workshops on Cellular Neural Networks and Their Applications (Budapest, Hungary, ; Munich, Germany, ; Rome, Italy, ); the book entitled: Cellular Neural Networks, which was edited by T.

Roska, J Author: Bing J. Sheu, Joongho Choi. There is also a book, "Cellular Neural Networks and Visual Computing Foundations and Applications", written by Leon Chua and Tamas Roska, which provides examples and exercises to help illustrates points in a manner uncommon for papers and journal articles.

Neural Networks and Deep Learning is a free online book. The book will teach you about: Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data Deep learning, a powerful set of techniques for learning in neural networks Neural networks and deep learning currently provide.

Cellular neural networks (CNN), first formulated by L.O. Chua, made their appearance in [1].Cited by: Introduction This book explores how neural networks can be designed to analyze sensory data in a way that mimics natural systems.

It introduces readers to the cellular neural network (CNN) and formulates it to match the behavior of the Wilson–Cowan model. This book deals with the way in which neural networks can be designed to analyze sensory data in a way which mimics a natural system.

demonstrates that reconfiguring a single-layer cellular neural network (CNN) is an easier and more flexible solution than the procedure required in a multilayer CNN. Cellular Neural Networks: Theory and Applications - Angela Slavova, Valeri Mladenov - Google Books This book deals with new theoretical results for studyingCellular Neural Networks (CNNs).Cellular neural networks: analysis, design, and synthesis [Book Review] Article in IEEE Circuits and Devices Magazine 17(5) October with 11 Reads How we measure 'reads'.Cellular Neural Networks and Analog VLSI brings together in one place important contributions and up-to-date research results in this fast moving area.

Cellular Neural Networks and Analog VLSI serves as an excellent reference, providing insight into some of the most challenging research issues in the field.