Estimating the varying topology of discrete-time dynamical networks with noise

Open Physics ◽  
2013 ◽  
Vol 11 (8) ◽  
Author(s):  
Chengyi Tu ◽  
Yuhua Cheng ◽  
Kai Chen
Keyword(s):  
Numerical Simulation ◽  
Parameter Estimation ◽  
Discrete Time ◽  
Moving Average ◽  
Improved Method ◽  
Dynamical Networks ◽  
Moving Average Filter ◽  
Influence Of Noise ◽  
Average Filter ◽  
Theoretical Results

AbstractWe propose an improved method to estimate the varying topology of discrete-time dynamical networks using autosynchronization. The networks considered in this paper can be weighted or unweighted and directed or undirected, and the dynamics of each node can be nonuniform. Furthermore, we suggest using a moving-average filter to suppress the influence of noise on parameter estimation. Finally, several examples are illustrated to verify the theoretical results by numerical simulation.

STABILITY OF TWO TYPICAL COMPLEX DYNAMICAL NETWORKS

2008 ◽  
Vol 22 (05) ◽  
pp. 553-560 ◽  
Author(s):  
WU-JIE YUAN ◽  
XIAO-SHU LUO ◽  
PIN-QUN JIANG ◽  
BING-HONG WANG ◽  
JIN-QING FANG
Keyword(s):  
Numerical Simulation ◽  
Dissipative System ◽  
Small World ◽  
Complex Dynamical Networks ◽  
Dynamical Networks ◽  
Scale Free ◽  
Scale Free Networks ◽  
General Complex ◽  
The Stability ◽  
Theoretical Results

When being constructed, complex dynamical networks can lose stability in the sense of Lyapunov (i. s. L.) due to positive feedback. Thus, there is much important worthiness in the theory and applications of complex dynamical networks to study the stability. In this paper, according to dissipative system criteria, we give the stability condition in general complex dynamical networks, especially, in NW small-world and BA scale-free networks. The results of theoretical analysis and numerical simulation show that the stability i. s. L. depends on the maximal connectivity of the network. Finally, we show a numerical example to verify our theoretical results.

Single and three phase moving average filter PLLs: Digital controller design recipe

2014 ◽  
Vol 116 ◽  
pp. 276-283 ◽  
Author(s):  
Naji Rajai Nasri Ama ◽  
Wilson Komatsu ◽  
Lourenco Matakas Junior
Keyword(s):  
Controller Design ◽  
Moving Average ◽  
Digital Controller ◽  
Moving Average Filter ◽  
Three Phase ◽  
Average Filter

scholarly journals Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3997 ◽  
Author(s):  
Tam Nguyen ◽  
Xiaoli Qin ◽  
Anh Dinh ◽  
Francis Bui
Keyword(s):  
Template Matching ◽  
Moving Average ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Moving Average Filter ◽  
Ecg Signals ◽  
Low Resource ◽  
Peak Detection Algorithm ◽  
Robustness To Noise ◽  
Average Filter

A novel R-peak detection algorithm suitable for wearable electrocardiogram (ECG) devices is proposed with four objectives: robustness to noise, low latency processing, low resource complexity, and automatic tuning of parameters. The approach is a two-pronged algorithm comprising (1) triangle template matching to accentuate the slope information of the R-peaks and (2) a single moving average filter to define a dynamic threshold for peak detection. The proposed algorithm was validated on eight ECG public databases. The obtained results not only presented good accuracy, but also low resource complexity, all of which show great potential for detection R-peaks in ECG signals collected from wearable devices.

Continuous Autoregressive Moving Average Models

2021 ◽  
pp. 118-141
Author(s):  
Yakup Ari
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Discrete Time ◽  
Moving Average ◽  
Likelihood Estimation ◽  
Real Data ◽  
Autoregressive Moving Average ◽  
The Difference

The financial time series have a high frequency and the difference between their observations is not regular. Therefore, continuous models can be used instead of discrete-time series models. The purpose of this chapter is to define Lévy-driven continuous autoregressive moving average (CARMA) models and their applications. The CARMA model is an explicit solution to stochastic differential equations, and also, it is analogue to the discrete ARMA models. In order to form a basis for CARMA processes, the structures of discrete-time processes models are examined. Then stochastic differential equations, Lévy processes, compound Poisson processes, and variance gamma processes are defined. Finally, the parameter estimation of CARMA(2,1) is discussed as an example. The most common method for the parameter estimation of the CARMA process is the pseudo maximum likelihood estimation (PMLE) method by mapping the ARMA coefficients to the corresponding estimates of the CARMA coefficients. Furthermore, a simulation study and a real data application are given as examples.

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