2013
DOI: 10.1016/j.neucom.2013.02.020
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Stochastic stability analysis of competitive neural networks with different time-scales

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Cited by 22 publications
(8 citation statements)
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“…So there are two timescales in the competitive neural networks, one corresponding to the fast change of the state and the other to the slow change of the synapse by external stimuli. Meyer-Bäse et al (1996;Meyer-Bäse, Botella, & Rybarska-Rusinek, 2013;Meyer-Bäse, Pilyugin, & Chen, 2003;Meyer-Bäse, Pilyugin, Wismler, & Foo, 2004) studied the stability of competitive neural networks with different timescales that were not delayed. However, time delays exist in real neural networks and always influence the information processing of neurons for various reasons; they may, for example, cause periodic oscillations, bifurcation, or chaotic attractors.…”
Section: Introductionmentioning
confidence: 99%
“…So there are two timescales in the competitive neural networks, one corresponding to the fast change of the state and the other to the slow change of the synapse by external stimuli. Meyer-Bäse et al (1996;Meyer-Bäse, Botella, & Rybarska-Rusinek, 2013;Meyer-Bäse, Pilyugin, & Chen, 2003;Meyer-Bäse, Pilyugin, Wismler, & Foo, 2004) studied the stability of competitive neural networks with different timescales that were not delayed. However, time delays exist in real neural networks and always influence the information processing of neurons for various reasons; they may, for example, cause periodic oscillations, bifurcation, or chaotic attractors.…”
Section: Introductionmentioning
confidence: 99%
“…Kondekar et al [21] provide a mapreduce based hybrid genetic solution for solving large-scale vehicle routing problems in dynamic network with fluctuant link travel time. Neural network is also applied to solve stochastic multiconstraint problems with different time-scales; see Zhang et al [22] and Meyer-Bäse et al [23]. Demir et al [24] proposed an adaptive large neighborhood search algorithm (ALNS) to minimize the fuel consumption and the driving time with Pareto optimality.…”
Section: Introductionmentioning
confidence: 99%
“…Hoffman et al [17] used field programmable gate arrays and fixed-point numbers for hardware synthesis of artificial neural networks and a significantly sized artificial neural network for the classic character recognition problem has been synthesized for FPGA (Field-Programmable Gate Array) hardware and simulated. Meyer-Bäse et al [18] determined conditions that ensure the existence of the exponentially mean-square stability equilibria of the stochastic nonlinear system. It is assumed that the system is described by Ito-type equations.…”
Section: Introductionmentioning
confidence: 99%