SELECTION OF SPIRAL WAVE IN THE COUPLED NETWORK UNDER GAUSSIAN COLORED NOISE

2013 ◽  
Vol 27 (21) ◽  
pp. 1350115 ◽  
Author(s):  
FAN LI ◽  
JUN MA
Keyword(s):  
Correlation Time ◽  
Colored Noise ◽  
Nearest Neighbor ◽  
Spiral Wave ◽  
External Noise ◽  
Parameter Region ◽  
Chua Circuit ◽  
Gaussian Colored Noise ◽  
Connection Type ◽  
Selection Of

The selection and breakup of spiral wave in a coupled network is investigated by imposing Gaussian colored noise on the network, respectively. The dynamics of each node of the network is described by a simplified Chua circuit, and nodes are uniformly placed in a two-dimensional array with nearest-neighbor connection type. The transition of spiral wave is detected by changing the coupling intensity, intensity and correlation time τ in the noise. A statistical variable is used to discern the parameter region for breakup of spiral wave and robustness to external noise. Spiral waves emerge in the network when the network with structure of complex-periodic and chaotic properties. It is found that asymmetric coupling can induce deformation of spiral wave, stronger intensity or smaller correlation time in noise does cause breakup of the spiral wave.

NON-GAUSSIAN NOISE-INDUCED FIRING TRANSITIONS AND ORDERED BURSTING IN A THERMO-SENSITIVE NEURON

2010 ◽  
Vol 09 (03) ◽  
pp. 289-299 ◽  
Author(s):  
YUBING GONG ◽  
XIU LIN ◽  
YINGHANG HAO ◽  
YANHANG XIE ◽  
XIAOGUANG MA
Keyword(s):  
Information Processing ◽  
Gaussian Noise ◽  
Correlation Time ◽  
Colored Noise ◽  
Firing Activity ◽  
Firing Behavior ◽  
Gaussian Colored Noise ◽  
Non Gaussian

In this letter, we investigate how a particular kind of non-Gaussian colored noise (NGN), especially the correlation time τ and the departure q from Gaussian noise, affects the chaotic firing behavior in a thermo-sensitive neuron. It is found that transitions between spiking and bursting occur with changing τ or q, and ordered bursting appears when τ is optimal. As τ is increased, the neuron alternately exhibits spiking and bursting when q < 1, but always bursts when q > 1, and chaotic bursts may become ordered at an optimal τ. As q is increased, the neuron also exhibits transitions between spiking and bursting. These findings provide a new mechanism for the firing transitions in the neuron and present the constructive role of the NGN in the firing activity in the neuron. This reveals that the NGN would play subtle roles in the communication and information processing in the neurons.

Coherence resonance in the two-dimensional neural map driven by non-Gaussian colored noise

2016 ◽  
Vol 30 (05) ◽  
pp. 1650012 ◽  
Author(s):  
Dongxi Li ◽  
Bing Hu ◽  
Jia Wang ◽  
Yingchuan Jing ◽  
Fangmei Hou
Keyword(s):  
Correlation Time ◽  
Discrete System ◽  
Noise Intensity ◽  
Colored Noise ◽  
Two Dimensional ◽  
Coherence Resonance ◽  
Firing Activity ◽  
Gaussian Colored Noise ◽  
Coherence Parameter ◽  
Non Gaussian

Based on the two-dimensional (2D) neural map, we investigate the impacts of non-Gaussian colored noise on the firing activity of discrete system. Taking the coherence parameter R to measure the regularity of firing behavior, it is demonstrated that coherence parameter R has a pronounced minimum value with the noise intensity and the correlation time of non-Gaussian colored noise, which is the so-called phenomenon of coherence resonance (CR). Besides, the firing activity is not sensitive to the non-Gaussian parameter which determines the departure from the Gaussian distribution when the correlation time is large enough.

Nano-friction phenomenon of Frenkel-Kontorova model under Gaussian colored noise

2021 ◽  
Author(s):  
Li Yi-Wei ◽  
Xu Peng-Fei ◽  
Yang Yong-Ge
Keyword(s):  
Friction Force ◽  
Correlation Time ◽  
Noise Intensity ◽  
Colored Noise ◽  
Static Friction ◽  
Simulation Method ◽  
Stochastic Force ◽  
Static Friction Force ◽  
Gaussian Colored Noise ◽  
Kontorova Model

Abstract The nano-friction phenomenon in a one-dimensional Frenkel-Kontorova model under Gaussian colored noise is investigated by using the molecular dynamic simulation method. The role of colored noise is analyzed through the inclusion of a stochastic force via a Langevin molecular dynamics method. Via the stochastic Runge-Kutta algorithm, the relationship between different parameter values of the Gaussian colored noise (the noise intensity and the correlation time) and the nano-friction phenomena such as hysteresis, the maximum static friction force is separately studied here. Similar results are obtained from the two geometrically opposed ideal cases: incommensurate and commensurate interfaces. It was found that the noise strongly influences the hysteresis and maximum static friction force and with an appropriate external driving force, the introduction of noise can accelerate the motion of the system, making the atoms escape from the substrate potential well more easily. Interestingly, suitable correlation time and noise intensity give rise to super-lubricity. It is noteworthy that the difference between the two circumstances lies in the fact that the effect of the noise is much stronger on triggering the motion of the FK model for the commensurate interface than that for the Incommensurate interface.

A Case Based Reasoning System For Recommendation Of Restaurant In Jimbaran Using K-Nearest Neighbor

2020 ◽  
Vol 9 (2) ◽  
pp. 267
Author(s):  
I Gede Teguh Mahardika ◽  
I Wayan Supriana
Keyword(s):  
Recommendation System ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Retrieval Process ◽  
K Nearest Neighbor ◽  
Reasoning System ◽  
Human Ability ◽  
And Function ◽  
Case Based ◽  
Selection Of

Culinary is one of the favorite businesses today. The number of considerations to choose a restaurant or place to visit becomes one of the factors that is difficult to determine the restaurant or place to eat. To get the desired place to eat advice, one needs a recommendation system. Decisions made by the recommendation system can be used as a reference to determine the choice of restaurants. One method that can be used to build a recommendation system is Case Based Reasoning. The Case Based Reasoning (CBR) method mimics human ability to solve a problem or cases. The retrieval process is the most important stage, because at this stage the search for a solution for a new case is carried out. The study used the K-Nearest Neighbor method to find closeness between new cases and case bases. With the selection of features used as domains in the system, the results of recommendations presented can be more suggestive and accurate. The system successfully provides complex recommendations based on the type and type of food entered by the user. Based on blackbox testing, the system has features that can be used and function properly according to the purpose of creating the system.

scholarly journals Simultaneous Selection of Features and Metric for Optimal Nearest Neighbor Classification

2004 ◽  
Vol 33 (9) ◽  
pp. 2137-2157 ◽  
Author(s):  
David A. Johannsen ◽  
Edward J. Wegman ◽  
Jeffrey L. Solka ◽  
Carey E. Priebe
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbor Classification ◽  
Simultaneous Selection ◽  
Neighbor Classification ◽  
Selection Of

scholarly journals Optimal FPE for non-linear 1d-SDE. I: Additive Gaussian colored noise

2020 ◽  
Vol 4 (10) ◽  
pp. 105019
Author(s):  
Marco Bianucci ◽  
Riccardo Mannella
Keyword(s):  
Colored Noise ◽  
Gaussian Colored Noise ◽  
Non Linear

scholarly journals Selection of Pairings Reaching Evenly Across the Data (SPREAD): A simple algorithm to design maximally informative fully crossed mating experiments

2014 ◽  
Author(s):  
Kolea Zimmerman ◽  
Daniel Levitis ◽  
Ethan Addicott ◽  
Anne Pringle
Keyword(s):  
Nearest Neighbor ◽  
Simple Algorithm ◽  
Two Dimensional ◽  
Neighbor Distance ◽  
Crossing Experiments ◽  
The Mean ◽  
Parameter Values ◽  
Selection Of ◽  
Novel Algorithm ◽  
Trait Space

We present a novel algorithm for the design of crossing experiments. The algorithm identifies a set of individuals (a ?crossing-set?) from a larger pool of potential crossing-sets by maximizing the diversity of traits of interest, for example, maximizing the range of genetic and geographic distances between individuals included in the crossing-set. To calculate diversity, we use the mean nearest neighbor distance of crosses plotted in trait space. We implement our algorithm on a real dataset ofNeurospora crassastrains, using the genetic and geographic distances between potential crosses as a two-dimensional trait space. In simulated mating experiments, crossing-sets selected by our algorithm provide better estimates of underlying parameter values than randomly chosen crossing-sets.

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