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A New Synthesis Approach for Feedback Neural Networks Based on the Perceptron Training Algorithm
, 1997
"...  In the present paper, a new synthesis approach is developed for associative memories based on the perceptron training algorithm. The design (synthesis) problem of feedback neural networks for associative memories is formulated as a set of linear inequalities such that the use of perceptron trainin ..."
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Cited by 5 (2 self)
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 In the present paper, a new synthesis approach is developed for associative memories based on the perceptron training algorithm. The design (synthesis) problem of feedback neural networks for associative memories is formulated as a set of linear inequalities such that the use of perceptron training is evident. The perceptron training in the synthesis algorithms is guaranteed to converge for the design of neural networks without any constraints on the connection matrix. For neural networks with constraints on the diagonal elements of the connection matrix, results concerning the properties of such networks and concerning the existence of such a network design are established. For neural networks with sparsity and/or symmetry constraints on the connection matrix, design algorithms are presented. Applications of the present synthesis approach to the design of associative memories realized by means of other feedback neural network models are studied. To demonstrate the applicability of t...
An Exact and Direct Analytical Method for the Design of Optimally Robust CNN Templates
 IEEE TRANS. CIRCUITS & SYST.I
, 1999
"... In this paper, we present an analytical design approach for the class of bipolar cellular neural networks (CNN's) which yields optimally robust template parameters. We give a rigorous definition of absolute and relative robustness and show that all welldefined CNN tasks are characterized by a finit ..."
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Cited by 5 (2 self)
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In this paper, we present an analytical design approach for the class of bipolar cellular neural networks (CNN's) which yields optimally robust template parameters. We give a rigorous definition of absolute and relative robustness and show that all welldefined CNN tasks are characterized by a finite set of linear and homogeneous inequalities. This system of inequalities can be analytically solved for the most robust template by simple matrix algebra. For the relative robustness of a task, a theoretical upper bound exists and is easily derived, whereas the absolute robustness can be arbitrarily increased by template scaling. A series of examples demonstrates the simplicity and broad applicability of the proposed method.
An Analysis of CNN Settling Time
, 1998
"... The settling time of cellular neural networks (CNNs) is crucial for both simulation and applications of VLSI CNN chips. The computational effort for the numerical integration may be drastically reduced, and CNN programs can be optimized, if a priori knowledge on the settling time is available. Moreo ..."
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Cited by 1 (0 self)
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The settling time of cellular neural networks (CNNs) is crucial for both simulation and applications of VLSI CNN chips. The computational effort for the numerical integration may be drastically reduced, and CNN programs can be optimized, if a priori knowledge on the settling time is available. Moreover, this allows the parameters necessary to achieve higher processing speed to be tuned. For certain template classes, we present analytic solutions, while for others, tight upper bounds are given. 1. INTRODUCTION In this paper, we consider the class of singlelayer, spatially invariant cellular neural networks (CNNs) with neighborhood radius one, following the definition given in [1]. The dynamics of the network is governed by a system of n = MNdifferential equations, d x i (t) d t =x i (t) + X k#N i a k f (x k (t)) +b k u k + I + # i ,(1) where N i denotes the neighborhood of the cell C i , a k and b k the template parameters, and # i the contribution from the boundar...
Corrections to "Chaotic Complex Spreading Sequences for Asynchronous DSCDMAPart 1: System Modeling and Results"
, 1998
"... We investigate the issue of robustness and how it is effected by the choice of boundary values for a given template set. First we introduce a measure of robustness, and then we show that, in some cases, an appropriate choice of boundary value may increase the attainable robustness. In other cases, t ..."
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We investigate the issue of robustness and how it is effected by the choice of boundary values for a given template set. First we introduce a measure of robustness, and then we show that, in some cases, an appropriate choice of boundary value may increase the attainable robustness. In other cases, the boundary value may be varied within some interval without loss of robustness. I. INTRODUCTION Cellular neural networks (CNN's) constitute a class of recurrent networks that can be implemented in analog VLSI technology [1], [2]. The dynamics of each cell is governed by C dx ij (t) dt = 0 1 R x ij (t)+ kl 2N a ij;kl sat(x kl (t)) + kl 2N b ij;kl u kl + I (1) Manuscript received July 24, 1996; revised December 20, 1996 and April 28, 1997. This paper was recommended by Associate Editor J. PinedadeGyvez. B. Mirzai is with the Signal and Information Processing Laboratory, Swiss Federal Institute of Technology, Zurich, Switzerland (email: mirzai@isi.ee.ethz.ch). G. S. Moschytz ...
Optimization of CNN Template Robustness
, 1999
"... Introduction 1.1 The Classo Bip Cellular Neural Netwo0A In this letter, weco00b the classo singlelayer, spatially invariant cellular neural netwo05 (CNNs) with neighbogho d radiusodi foiu wing thedefinitio given in [1]. The dynamicso the netwo isgo verned by a systemo MN di#erentialequatio5b ..."
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Introduction 1.1 The Classo Bip Cellular Neural Netwo0A In this letter, weco00b the classo singlelayer, spatially invariant cellular neural netwo05 (CNNs) with neighbogho d radiusodi foiu wing thedefinitio given in [1]. The dynamicso the netwo isgo verned by a systemo MN di#erentialequatio5b dx i (t) dt = x i (t)+ # k#N i # a k f(x k (t)) + b k u k # + I (1) where N idenob the neighoig o d o the cell C i , A = {a k } and B = {b k } the feed ack and co tro template parameters, respectively. f() is