Design of associative memories using cellular neural networks

Delbem, Alexandre C. B.; Correa, Leonardo Garcia; Zhao, Liang

doi:10.1016/j.neucom.2008.06.029

Cited by 19 publications

(2 citation statements)

References 14 publications

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“…During the past decades, most research work on CNNs focused on two main fields. One is on CNN theories, such as the stability analysis [3][4][5][6][7], the convergence analysis [8], the chaos [9,10] and the improved CNN models [11][12][13][14][15]; another is on CNN applications, such as the hardware implementations [16][17][18], the image processing [19][20][21], the pattern recognition [22,23] and the associative memories [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

One-dimensional pairwise CNN for the global alignment of two DNA sequences

et al. 2015

Neurocomputing

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Section: Introductionmentioning

confidence: 99%

One-dimensional pairwise CNN for the global alignment of two DNA sequences

et al. 2015

Neurocomputing

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“…Cellular neural networks (CNNs) are large scale nonlinear circuits composed of locally connected cells, which was introduced in 1988 by Chua and Yang [1,2]. CNN has a tremendous variety of applications in the fields of dynamic systems and signal processing [3][4][5][6][7][8][9]. The analysis of the dynamic behavior for the class of CNNs without feedback interconnections from neighboring cells, namely the uncoupled CNNs, is one of the popular research topics.…”

Section: Introductionmentioning

confidence: 99%

Robust Template Decomposition without Weight Restriction for Cellular Neural Networks Implementing Arbitrary Boolean Functions Using Support Vector Classifiers

Lin

Hsieh

Jeng

2013

Mathematical Problems in Engineering

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If the given Boolean function is linearly separable, a robust uncoupled cellular neural network can be designed as a maximal margin classifier. On the other hand, if the given Boolean function is linearly separable but has a small geometric margin or it is not linearly separable, a popular approach is to find a sequence of robust uncoupled cellular neural networks implementing the given Boolean function. In the past research works using this approach, the control template parameters and thresholds are restricted to assume only a given finite set of integers, and this is certainly unnecessary for the template design. In this study, we try to remove this restriction. Minterm- and maxterm-based decomposition algorithms utilizing the soft margin and maximal margin support vector classifiers are proposed to design a sequence of robust templates implementing an arbitrary Boolean function. Several illustrative examples are simulated to demonstrate the efficiency of the proposed method by comparing our results with those produced by other decomposition methods with restricted weights.

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Associative Memory Networks

Swamy²

2013

Neural Networks and Statistical Learning

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Design of associative memories using cellular neural networks

Cited by 19 publications

References 14 publications

One-dimensional pairwise CNN for the global alignment of two DNA sequences

One-dimensional pairwise CNN for the global alignment of two DNA sequences

Robust Template Decomposition without Weight Restriction for Cellular Neural Networks Implementing Arbitrary Boolean Functions Using Support Vector Classifiers

Associative Memory Networks

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