nach oben

Neural Computing and Applications

Erschienen in:

01.06.2014 | Original Article

A winner-take-all Lotka–Volterra recurrent neural network with only one winner in each row and each column

verfasst von: Bochuan Zheng

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2014

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

A winner-take-all Lotka–Volterra recurrent neural network with N × N neurons is proposed in this paper. Sufficient conditions for existence of winner-take-all stable equilibrium points in the network are obtained. These conditions guarantee that there is one and only one winner in each row and each column at any stable equilibrium point. In addition, rigorous convergence analysis is carried out. It is proven that the proposed network model is convergent. The conditions for the winner-take-all behavior obtained in this paper provide design guidelines for network implementation and fabrication. Simulations are also presented to illustrate the theoretical findings.

Vorheriger Artikel Content-based image retrieval using extroverted semantics: a probabilistic approach

Nächster Artikel An improved SOM algorithm and its application to color feature extraction

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Urahama K, Nagao T (1995) k-Winner-take-all circuit with O(N) complexity. IEEE Trans Neural Netw 6:775–778CrossRef

Wang L (1999) Multi-associative neural networks and their applications to learning and retrieving complex spatio-temporal sequences. IEEE Trans Syst Man Cybern Part B Cybern 29:73–82CrossRef

Wang L (1997) On competitive learning. IEEE Trans Neural Netw 8:1214–1217CrossRef

Dempsey GL, McVey ES (1993) Circuit implementation of a peak detector neural network. IEEE Trans Circuits Syst II Analog Digital Signal Process 40:585–591CrossRef

Seiler G, Nossek JA (1993) Winner-take-all cellular neural networks. IEEE Trans Circuits Syst II Analog Digit Signal Process 40:184–190MathSciNetCrossRefMATH

Andrew LLH (1996) Improving the robustness of winner-take-all cellular neural networks. IEEE Trans Circuits Syst II Analog Digit Signal Process 43:329–334CrossRefMATH

Fukai T, Tanaka S (1997) A simple neural network exhibiting selective activation of neuronal ensembles: from winner-take-all to winner-share-all. Neural Comput 9:77–97CrossRefMATH

Asai T, Fukai T, Tanaka S (1999) A subthreshold MOS circuit for the Lotka–Volterra neural network producing the winner-take-all solutions. Neural Netw 12:211–216CrossRef

Hahnloser RHR (1998) On the piecewise analysis of networks of linear threshold neurons. Neural Netw 11:691–697CrossRef

10.

Tang HJ, Tan KC, Zhang W (2005) Analysis of cyclic dynamics for networks of linear threshold neurons. Neural Comput 17:97–114MathSciNetCrossRefMATH

11.

Qu H, Yi Z, Wang X (2009) A winner-take-all neural networks of N linear threshold neurons without self-excitatory connections. Neural Process Lett 29:143–154CrossRef

12.

Yi Z, Heng PA, Fung PF (2000) Winner-take-all discrete recurrent neural networks. IEEE Trans Circuits Syst II Analog Digit Signal Process 47:1584–1589CrossRef

13.

Wang J (2010) Analysis and design of a k-winners-take-all model with a single state variable and the heaviside step activation function. IEEE Trans Neural Netw 21:1496–1506CrossRef

14.

Liu Q, Dang C, Cao J (2010) A novel recurrent neural network with one neuron and finite-time convergence for k-winners-take-all operation. IEEE Trans Neural Netw 21:1140–1148CrossRef

15.

Yang JF, Chen CM (1997) A dynamic k-winners-take-all neural network. IEEE Trans Syst Man Cybern Part B Cybern 27:523–526CrossRef

16.

Wersing H, Steil JJ, Ritter H (2001) A competitive layer model for feature binding and sensory segmentation. Neural Comput 13:357–387CrossRefMATH

17.

Yi Z (2010) Foundations of implementing the competitive layer model by Lotka–Volterra recurrent neural networks. IEEE Trans Neural Netw 21:494–507CrossRef

18.

Zheng B, Yi Z (2012) A new method based on the CLM of the LV RNN for brain MR image segmentation. Digit Signal Process 22:497–505MathSciNetCrossRef

19.

Yi Z, Tan KK (2004) Convergence analysis of recurrent neural networks. Kluwer, DordrechtCrossRefMATH

Titel: A winner-take-all Lotka–Volterra recurrent neural network with only one winner in each row and each column
verfasst von: Bochuan Zheng
Publikationsdatum: 01.06.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7-8/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-013-1412-0

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 7-8/2014

Monthly flow forecast for Mississippi River basin using artificial neural networks

Situation recognition using image moments and recurrent neural networks

Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components

Learning high-dimensional correspondence via manifold learning and local approximation

Adaptive control for a class of chemical reactor systems in discrete-time form

Fault detection and measurements correction for multiple sensors using a modified autoassociative neural network

Premium Partner