Role of network topology in noise reduction using coupled dynamics

Kohar, Vivek; Kia, Sarvenaz; Kia, Behnam; Lindner, John F.; Ditto, William L.

doi:10.1007/s11071-016-2607-6

Cited by 8 publications

(5 citation statements)

References 33 publications

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“…Depending on the coupling topology, the deviations in some nodes may be affected more compared than others. Thus all nodes may be equally robust to noise or the noise robustness may vary among the nodes depending on their neighborhood [24].…”

Section: Flowsmentioning

confidence: 99%

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Superlinearly scalable noise robustness of redundant coupled dynamical systems

et al. 2016

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We illustrate through theory and numerical simulations that redundant coupled dynamical systems can be extremely robust against local noise in comparison to uncoupled dynamical systems evolving in the same noisy environment. Previous studies have shown that the noise robustness of redundant coupled dynamical systems is linearly scalable and deviations due to noise can be minimized by increasing the number of coupled units. Here, we demonstrate that the noise robustness can actually be scaled superlinearly if some conditions are met and very high noise robustness can be realized with very few coupled units. We discuss these conditions and show that this superlinear scalability depends on the nonlinearity of the individual dynamical units. The phenomenon is demonstrated in discrete as well as continuous dynamical systems. This superlinear scalability not only provides us an opportunity to exploit the nonlinearity of physical systems without being bogged down by noise but may also help us in understanding the functional role of coupled redundancy found in many biological systems. Moreover, engineers can exploit superlinear noise suppression by starting a coupled system near (not necessarily at) the appropriate initial condition.

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Section: Flowsmentioning

confidence: 99%

“…The role of coupled dynamics as a noise reduction mechanism has been studied recently [8,[19][20][21][22][23][24]. Coupling between redundant nonlinear dynamical systems can enhance the noise robustness and has been shown to play an important part in neural learning and decision making [21].…”

Section: Introductionmentioning

confidence: 99%

Superlinearly scalable noise robustness of redundant coupled dynamical systems

et al. 2016

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“…Konishi et al [11] presented a car-following CML model for suppression of traffic congestion. Kohar et al [12] used a quadratic CML method to research the role of network topology in noise reduction. For cascading failures, its inner essence is that a single node's failure is possible to spread to the entire network due to the coupled relationship with others.…”

Section: Introductionmentioning

confidence: 99%

Analysis on Invulnerability of Wireless Sensor Network towards Cascading Failures Based on Coupled Map Lattice

Yang

Yao

2018

Complexity

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Previous research of wireless sensor networks (WSNs) invulnerability mainly focuses on the static topology, while ignoring the cascading process of the network caused by the dynamic changes of load. Therefore, given the realistic features of WSNs, in this paper we research the invulnerability of WSNs with respect to cascading failures based on the coupled map lattice (CML). The invulnerability and the cascading process of four types of network topologies (i.e., random network, small-world network, homogenous scale-free network, and heterogeneous scale-free network) under various attack schemes (i.e., random attack, maxdegree attack, and max-status attack) are investigated, respectively. The simulation results demonstrate that the rise of interference and coupling coefficient will increase the risks of cascading failures. Cascading threshold values and exist, where cascading failures will spread to the entire network when > or > . When facing a random attack or max-status attack, the network with higher heterogeneity tends to have a stronger invulnerability towards cascading failures. Conversely, when facing a max-degree attack, the network with higher uniformity tends to have a better performance. Besides that, we have also proved that the spreading speed of cascading failures is inversely proportional to the average path length of the network and the increase of average degree ⟨ ⟩ can improve the network invulnerability.

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“…The number of implementable functions increases exponentially with the number of iterations [5], so it is crucial that we can maximize the iterations for which output can be obtained successfully. One method to achieve this objective is to suppress the noise evolution through coupled redundant nonlinear circuits as the local noise in different circuits averages out and the overall deviation in the output is reduced [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Implementing Boolean Functions in Hybrid Digital-Analog Systems

et al. 2017

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We propose an architecture to implement multi−input one−output Boolean functions using chaos computing in hybrid digital-analog systems consisting of a digital block of conventional AND gates and a nonlinear circuit. This architecture efficiently utilizes the super-stable initial conditions of a nonlinear circuit and enables us to implement all possible 2 2 m Boolean functions of m data inputs in just m iterations of the nonlinear circuit resulting in better operating speed and noise tolerance. In an ideal nonlinear map, this architecture eliminates the need for a decoder as the outputs are mapped to maxima and minima of the map and can be fed directly to the next stage enabling multilayer concatenation. We demonstrate the utility of this architecture in a 3−transistor circuit.

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Role of network topology in noise reduction using coupled dynamics

Cited by 8 publications

References 33 publications

Superlinearly scalable noise robustness of redundant coupled dynamical systems

Superlinearly scalable noise robustness of redundant coupled dynamical systems

Analysis on Invulnerability of Wireless Sensor Network towards Cascading Failures Based on Coupled Map Lattice

Implementing Boolean Functions in Hybrid Digital-Analog Systems

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