Circuit implementation of a peak detector neural network

Dempsey, G.L.; McVey, Eugene S.

doi:10.1109/82.257342

Cited by 25 publications

(6 citation statements)

References 15 publications

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“…In recent years, several effective kWTA networks were developed based on the quadratic programming problem formulation (6). For example, a kWTA neural network with n neurons was tailored from the simplified dual network for quadratic programming [27] state equation…”

Section: Recent Modelsmentioning

confidence: 99%

Analysis and Design of a $k$-Winners-Take-All Model With a Single State Variable and the Heaviside Step Activation Function

Wang

2010

IEEE Trans. Neural Netw.

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This paper presents a k-winners-take-all (kWTA) neural network with a single state variable and a hard-limiting activation function. First, following several kWTA problem formulations, related existing kWTA networks are reviewed. Then, the kWTA model model with a single state variable and a Heaviside step activation function is described and its global stability and finite-time convergence are proven with derived upper and lower bounds. In addition, the initial state estimation and a discrete-time version of the kWTA model are discussed. Furthermore, two selected applications to parallel sorting and rank-order filtering based on the kWTA model are discussed. Finally, simulation results show the effectiveness and performance of the kWTA model.

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Section: Recent Modelsmentioning

confidence: 99%

Analysis and Design of a $k$-Winners-Take-All Model With a Single State Variable and the Heaviside Step Activation Function

Wang

2010

IEEE Trans. Neural Netw.

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“…Other KW T A algorithms of discrete time type can be found in [7]- [11]. Concrete hardware realizations can be found in [12], [13] and [18]. The K = 1 case has a larger bibliography, see [17] and [19].…”

Section: Introductionmentioning

confidence: 99%

Recurrent neural network as a KWTA selector: A synthesis procedure

Costea

Marinov

2011

2011 IEEE International Symposium of Circuits and Systems (ISCAS)

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“…Winner-take-all networks have been studied widely by many authors in recent years, see for examples [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. It is well known that they play an important role in the design for many unsupervised learning networks.…”

Section: Introductionmentioning

confidence: 99%

Winner-take-all discrete recurrent neural networks

Zhang

Heng

Fung

2000

IEEE Trans. Circuits Syst. II

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This paper proposes a discrete recurrent neural network model to implement winnertake-all function. This network model has simple organizations and clear dynamic behaviours. The dynamic properties of the proposed winner-take-all networks are studied in detail. Simulation results are given to show network performance. Since the network model is formulated as discrete time systems, it has advantages for computer simulations over digital simulations of continuous time neural network model. Thus they can be easily implemented in digital hardware. Index Terms | Winner-take-all neural networks, discrete recurrent neural networks, network response time.

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Circuit implementation of a peak detector neural network

Cited by 25 publications

References 15 publications

Analysis and Design of a $k$-Winners-Take-All Model With a Single State Variable and the Heaviside Step Activation Function

Analysis and Design of a $k$-Winners-Take-All Model With a Single State Variable and the Heaviside Step Activation Function

Recurrent neural network as a KWTA selector: A synthesis procedure

Winner-take-all discrete recurrent neural networks

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