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Neural Computing and Applications

Erschienen in:

01.06.2014 | Original Article

Analysis and design of winner-take-all behavior based on a novel memristive neural network

verfasst von: Ailong Wu, Zhigang Zeng, Jiejie Chen

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

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Abstract

In this paper, some sufficient conditions are derived to guarantee a novel memristive neural network for realizing winner-take-all behavior. Some design methods for synthesizing the winner-take-all behavior based on the memristive neural network are developed by using the obtained results. Finally, simulation results demonstrate the validity and characteristics of the proposed approach.

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Burwick T (2009) On the relevance of local synchronization for establishing a winner-take-all functionality of the gamma cycle. Neurocomputing 72(7–9):1525–1533CrossRef

Cai XD, Prokhorov DV, Wunsch DC (2007) Training winner-take-all simultaneous recurrent neural networks. IEEE Trans Neural Netw 18(3):674–684CrossRef

Costea RL, Marinov CA (2011) New accurate and flexible design procedure for a stable KWTA continuous time network. IEEE Trans Neural Netw 22(9):1357–1367CrossRef

Fang YG, Cohen MA, Kincaid TG (2010) Dynamic analysis of a general class of winner-take-all competitive neural networks. IEEE Trans Neural Netw 21(5):771–783CrossRef

Mao ZH, Massaquoi SG (2007) Dynamics of winner-take-all competition in recurrent neural networks with lateral inhibition. IEEE Trans Neural Netw 18(1):55–69CrossRef

Qu H, Yi Z, Wang XB (2009) A winner-take-all neural networks of N linear threshold neurons without self-excitatory connections. Neural Process Lett 29(3):143–154CrossRef

Hafliger P (2007) Adaptive WTA with an analog VLSI neuromorphic learning chip. IEEE Trans Neural Netw 18(2):551–572CrossRef

Marinov CA, Costea RL (2010) Time-oriented synthesis for a WTA continuous-time neural network affected by capacitive cross-coupling. IEEE Trans Circuits Syst I Reg Pap 57(6):1358–1370MathSciNetCrossRef

Oster M, Wang YX, Douglas R, Liu SC (2008) Quantification of a spike-based winner-take-all VLSI network. IEEE Trans Circuits Syst I Reg Pap 55(10):3160–3169MathSciNetCrossRef

10.

Hu XL, Wang J (2008) An improved dual neural network for solving a class of quadratic programming problems and its k-winners-take-all application. IEEE Trans Neural Netw 19(12):2022–2031CrossRef

11.

Liu SB, Wang J (2006) A simplified dual neural network for quadratic programming with its KWTA application. IEEE Trans Neural Netw 17(6):1500–1510CrossRef

12.

Liu QS, Wang J (2008) Two k-winners-take-all networks with discontinuous activation functions. Neural Netw 21(2–3):406–413

13.

Wang J (2010) Analysis and design of a k-winners-take-all model with a single state variable and Heaviside step activation function. IEEE Trans Neural Netw 21(9):1496–1506CrossRef

14.

Cantley KD, Subramaniam A, Stiegler HJ, Chapman RA, Vogel EM (2011) Hebbian learning in spiking neural networks with nanocrystalline silicon TFTs and memristive synapses. IEEE Trans Nanotechnol 10(5):1066–1073CrossRef

15.

Cantley KD, Subramaniam A, Stiegler HJ, Chapman RA, Vogel EM (2012) Neural learning crcuits utilizing nano-crystalline silicon transistors and memristors. IEEE Trans Neural Netw Learn Syst 23(4):565–573CrossRef

16.

Itoh M, Chua LO (2009) Memristor cellular automata and memristor discrete-time cellular neural networks. Int J Bifurcat Chaos 19(11):3605–3656MathSciNetCrossRefMATH

17.

Kim H, Sah MP, Yang CJ, Roska T, Chua LO (2012) Neural synaptic weighting with a pulse-based memristor circuit. IEEE Trans Circuits Syst I Reg Pap 59(1):148–158MathSciNetCrossRef

18.

Pershin YV, Di Ventra M (2010) Experimental demonstration of associative memory with memristive neural networks. Neural Netw 23(7):881–886CrossRef

19.

Wen SP, Zeng ZG (2012) Dynamics analysis of a class of memristor-based recurrent networks with time-varying delays in the presence of strong external stimuli. Neural Process Lett 35(1):47–59CrossRef

20.

Wu AL, Zeng ZG (2012) Exponential stabilization of memristive neural networks with time delays. IEEE Trans Neural Netw Learn Syst 23(12):1919–1929MathSciNetCrossRef

21.

Wu AL, Zeng ZG (2012) Dynamic behaviors of memristor-based recurrent neural networks with time-varying delays. Neural Netw 36:1–10CrossRefMATH

22.

Huang TW, Li CD, Duan SK, Starzyk JA (2012) Robust exponential stability of uncertain delayed neural networks with stochastic perturbation and impulse effects. IEEE Trans Neural Netw Learn Syst 23(6):866–875CrossRef

23.

Huang TW (2011) Robust stability of delayed fuzzy Cohen-Grossberg neural networks. Comput Math Appl 61(8):2247–2250MathSciNetCrossRefMATH

24.

Zhang HG, Wang YC (2008) Stability analysis of Markovian jumping stochastic Cohen-Grossberg neural networks with mixed time delays. IEEE Trans Neural Netw 19(2):366–370CrossRef

25.

Zhang HG, Liu JH, Ma DZ, Wang ZS (2011) Data-core-based fuzzy min-max neural network for pattern classification. IEEE Trans Neural Netw 22(12):2339–2352CrossRef

26.

Zhang HG, Ma TD, Huang GB, Wang ZL (2010) Robust global exponential synchronization of uncertain chaotic delayed neural networks via dual-stage impulsive control. IEEE Trans Syst Man Cybern B Cybern 40(3):831–844CrossRef

27.

Yu W (2009) A novel fuzzy-neural-network modeling approach to crude-oil blending. IEEE Trans Control Syst Technol 17(6):1424–1431CrossRef

28.

Yu W, Li XO (2010) Automated nonlinear system modeling with multiple fuzzy neural networks and kernel smoothing. Int J Neural Syst 20(5):429–435CrossRef

29.

Shen Y, Wang J (2009) Almost sure exponential stability of recurrent neural networks with Markovian switching. IEEE Trans Neural Netw 20(5):840–855CrossRef

30.

Shen Y, Wang J (2012) Robustness analysis of global exponential stability of recurrent neural networks in the presence of time delays and random disturbances. IEEE Trans Neural Netw Learn Syst 23(1):87–96CrossRef

Titel: Analysis and design of winner-take-all behavior based on a novel memristive neural network
verfasst von: Ailong Wu
Zhigang Zeng
Jiejie Chen
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/s00521-013-1395-x

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