Improved exponential bidirectional associative memory

Chen, Songcan; Gao, Hang; Yan, Weiping

doi:10.1049/el:19970155

Cited by 17 publications

(10 citation statements)

References 3 publications

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“…The stability of Hopfield neural networks and BAM neural networks is discussed in a lot of recently published literature works [8][9][10][11][12], but the researchers about MAM neural networks are mainly focused on learning algorithms, fault tolerance, and retrieval efficiency of MAM neural networks [3][4][5][6]. To the best of our knowledge, the research on the theory of MAM neural networks was reported only in a few papers [13][14][15][16][17][18]. Chen et al proved the stability of some specific types of MAM neural networks in [13,14].…”

Section: Introductionmentioning

confidence: 99%

Multistability in a Multidirectional Associative Memory Neural Network with Delays

Wang

Zhou

2013

Journal of Applied Mathematics

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This paper focuses on the multidirectional associative memory (MAM) neural networks with m fields which is more advanced to realize associative memory. Based on the Brouwer fixed point theorem and Dini upper right derivative, it is confirmed that the multidirectional associative memory neural network can have 3 equilibria and 2 equilibria of them are stable, where l is a parameter associated with the number of neurons. Furthermore, an example is given to illustrate the effectiveness of the results.

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Section: Introductionmentioning

confidence: 99%

Multistability in a Multidirectional Associative Memory Neural Network with Delays

Wang

Zhou

2013

Journal of Applied Mathematics

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“…So far, the research of MAM has mainly focused on neural network learning algorithms and network architecture designs [1,8,9,13,21]. To the best of our knowledge, the theoretical research of MAM neural networks was only reported in the literatures [1], which proved the stability of the multivalued exponential MAM in synchronous and asynchronous update modes for neuron states by constructing a new energy function.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, the theoretical research of MAM neural networks was only reported in the literatures [1], which proved the stability of the multivalued exponential MAM in synchronous and asynchronous update modes for neuron states by constructing a new energy function. However, the following problems have not been solved yet: (i) The problem of existence: under what conditions does an MAM neural network have some states to store the data of an associative memory?…”

Section: Introductionmentioning

confidence: 99%

Existence and Exponential Stability of Multiple Periodic Solutions for a Multidirectional Associative Memory Neural Network

Zhou

Wang

Long

2012

Neural Process Lett

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The paper proposes several mathematical models of the multidirectional associative memory (MAM) neural network by analyzing its structure. A model of MAM with distributed delays is studied. Under some new assumptions on activation functions, 2 n 0 [m/2] invariant subsets of MAM are constructed. Then the existence and the exponential stability of 2 n 0 [m/2] periodic solutions located on invariant subsets are obtained by constructing a suitable Liapunov function and a Poincaré mapping. An estimating method of the exponential convergence rate is given. The obtained results are new to MAM neural networks. An example is given to illustrate the effectiveness of the results.

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“…Attempts to find the best training algorithm include Chen et al (1997) which uses an exponential rule, Shen and Cruz (2005) which uses genetic algorithms, Zheng et al (2005) which uses descending gradient method, Eom et al (2001) which uses linear programming techniques among others. In essence, the problem is to maximise the number of patterns superimposed on one memory medium, namely, the weights of connections in a correlation matrix.…”

Section: Introductionmentioning

confidence: 99%

Hardware architecture for a bidirectional hetero-associative Protein Processing Associative Memory

Qadir

Liu

Timmis

et al. 2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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This paper details an extension to an architecture for robust bidirectional hetero-associative recall. Our proposed Protein Processor Associative Memory (PPAM) is fundamentally different from the traditional processing methods which use arithmetic operations and consequently Arithmetic and Logic Units (ALUs). In this paper, we improve on our initial work addressing concerns surrounding hardware implementation. We present the improved computational architecture, coupled with a corresponding hardware architecture for implementation. Results of applying the hardware implementation on a small dataset are included, along with reports from synthesis tools about hardware utilisation.

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Improved exponential bidirectional associative memory

Cited by 17 publications

References 3 publications

Multistability in a Multidirectional Associative Memory Neural Network with Delays

Multistability in a Multidirectional Associative Memory Neural Network with Delays

Existence and Exponential Stability of Multiple Periodic Solutions for a Multidirectional Associative Memory Neural Network

Hardware architecture for a bidirectional hetero-associative Protein Processing Associative Memory

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