CHAOTIC DYNAMICS OF A NONLINEAR ELECTRONIC CONVERTER

2008 ◽  
Vol 18 (10) ◽  
pp. 2981-3000
Author(s):  
E. CAMPOS-CANTÓN ◽  
J. S. MURGUÍA ◽  
H. C. ROSU
Keyword(s):  
Time Series Data ◽  
Chaotic Behavior ◽  
Series Data ◽  
Chaotic Oscillator ◽  
Quantitative Results ◽  
Wavelet Transform Analysis ◽  
Experimental Values ◽  
The Mathematical Model ◽  
Nonlinear Converter ◽  
Good Agreement

The nonlinear electronic converter used by Rulkov and collaborators [Rulkov et al., 2001], which is the core of their chaotic oscillator, is modeled and simulated numerically by means of an appropriate direct relationship between the experimental values of the electronic components of the system and the mathematical model. This relationship allows us to analyze the chaotic behavior of the model in terms of a particular bifurcation parameter k. Varying the parameter k, quantitative results of the dynamics of the numerical system are presented, which are found to be in good agreement with the experimental measurements that we performed as well. Moreover, we show that this nonlinear converter belongs to a class of 3-D systems that can be mapped to the unfolded Chua's circuit. We also report a wavelet transform analysis of the experimental and numerical chaotic time series data of this chaotic system. The wavelet analysis provides us with information on such systems in terms of the concentration of energy which is the standard electromagnetic interpretation of the L2 norm of a given signal.

scholarly journals Risk Monitoring and Quantitative Results of Various Attributes of Machine Learning Algorithms with a Time Series Data

2019 ◽  
Vol 8 (11) ◽  
pp. 4018-4022
Keyword(s):  
Machine Learning ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Machine Learning Algorithm ◽  
Risk Modelling ◽  
Risk Monitoring ◽  
Quantitative Results

The aim of this research is to do risk modelling after analysis of twitter posts based on certain sentiment analysis. In this research we analyze posts of several users or a particular user to check whether they can be cause of concern to the society or not. Every sentiment like happy, sad, anger and other emotions are going to provide scaling of severity in the conclusion of final table on which machine learning algorithm is applied. The data which is put under the machine learning algorithms are been monitored over a period of time and it is related to a particular topic in an area

scholarly journals Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

2017 ◽  
Vol 24 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Zhe An ◽  
Daniel Rey ◽  
Jingxin Ye ◽  
Henry D. I. Abarbanel
Keyword(s):  
Time Delays ◽  
Time Series Data ◽  
Chaotic Behavior ◽  
Weather Prediction ◽  
Series Data ◽  
State Variables ◽  
Nonlinear Shallow Water Equations ◽  
Delayed Measurements ◽  
Lagrangian Drifter ◽  
Geophysical System

Abstract. The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of the full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. We show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.

scholarly journals Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research

2021 ◽  
Vol 12 ◽  
Author(s):  
Suran Liu ◽  
Yujie You ◽  
Zhaoqi Tong ◽  
Le Zhang
Keyword(s):  
Time Series ◽  
Predictive Model ◽  
Time Series Data ◽  
Chaotic Behavior ◽  
Flow Behavior ◽  
Series Data ◽  
High Dimensional ◽  
Future State ◽  
Disease Occurrence ◽  
The Future

It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series. We evaluate this EKATP by using a genomics time series with chaotic behavior, a proteomics time series with oscillating behavior and a metabolomics time series with flow behavior. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness and generalizability to predict the future state of a time series for multi-omics data.

scholarly journals A hybrid model for modelling the salinity of the Tafna River in Algeria

2019 ◽  
Vol 40 (1) ◽  
pp. 127-135 ◽  
Author(s):  
Khemissi Houari ◽  
Tarik Hartani ◽  
Boualem Remini ◽  
Abdelouhab Lefkir ◽  
Leila Abda ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Inference System ◽  
Squared Error ◽  
Good Agreement

Abstract In this paper, the capacity of an Adaptive-Network-Based Fuzzy Inference System (ANFIS) for predicting salinity of the Tafna River is investigated. Time series data of daily liquid flow and saline concentrations from the gauging station of Pierre du Chat (160801) were used for training, validation and testing the hybrid model. Different methods were used to test the accuracy of our results, i.e. coefficient of determination (R2), Nash–Sutcliffe efficiency coefficient (E), root of the mean squared error (RSR) and graphic techniques. The model produced satisfactory results and showed a very good agreement between the predicted and observed data, with R2 equal (88% for training, 78.01% validation and 80.00% for testing), E equal (85.84% for training, 82.51% validation and 78.17% for testing), and RSR equal (2% for training, 10% validation and 49% for testing).

Measurement of Chaotic Behavior of Operator Stabilizing an Inverted Pendulum and Its Fuzzy Identification from Time Series Data

2001 ◽  
Vol 13 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Yoshihiko Kawazoe ◽  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Chaotic Behavior ◽  
Series Data ◽  
Experimental Time ◽  
Experimental Time Series ◽  
Simulated Time ◽  
Simulated Time Series

This paper investigates the identification of the chaotic characteristics of human operation with individual difference and the skill difference from the experimental time series data by utilizing fuzzy inference. It shows how to construct rules automatically for a fuzzy controller from experimental time series data of each trial of each operator to identify a controller from human-generated decision-making data. The characteristics of each operator trial were identified fairly well from experimental time series data by utilizing fuzzy reasoning. It was shown that the estimated maximum Lyapunov exponents of simulated time series data using an identified fuzzy controller were positive against embedding dimensions, which means a chaotic phenomenon. It was also recognized that the simulated human behavior have a large amount of disorder according to the result of estimated entropy from the simulated time, series data.

Meta-Analysis of Single-Case Research via Multilevel Models: Fundamental Concepts and Methodological Considerations

2018 ◽  
Vol 44 (2) ◽  
pp. 265-295 ◽  
Author(s):  
Mariola Moeyaert ◽  
Rumen Manolov ◽  
Emily Rodabaugh
Keyword(s):  
Multilevel Models ◽  
Time Series Data ◽  
Single Case ◽  
Meta Analysis ◽  
Evidence Base ◽  
Series Data ◽  
Individual Level ◽  
Quantitative Results ◽  
The Individual ◽  
Methodological Considerations

Multilevel modeling is an approach that can be used to summarize single-case experimental design (SCED) data. Multilevel models were developed to analyze hierarchical structured data with units at a lower level nested within higher level units. SCEDs use time series data collected from multiple cases (or subjects) within a study that allow researchers to investigate intervention effectiveness at the individual level and also to investigate how these individual intervention effects change over time. There is an increased interest in the field regarding how SCEDs can be used to establish an evidence base for interventions by synthesizing data from a series of intervention studies. Although using multilevel models to meta-analyze SCED studies is promising, application is often hampered by being potentially excessively technical. First, this article provides an accessible description and overview of the potential of multilevel meta-analysis to combine SCED data. Second, a summary of the methodological evidence on the performance of multilevel models for meta-analysis is provided, which is useful given that such evidence is currently scattered over multiple technical articles in the literature. Third, the actual steps to perform a multilevel meta-analysis are outlined in a brief practical guide. Fourth, a suggestion for integrating the quantitative results with a visual representation is provided.

scholarly journals Fractal dimensions of wildfire spreading

2014 ◽  
Vol 21 (4) ◽  
pp. 815-823 ◽  
Author(s):  
S.-L. Wang ◽  
H.-I. Lee ◽  
S.-P. Li
Keyword(s):  
Time Series ◽  
Cellular Automata ◽  
Empirical Data ◽  
Time Series Data ◽  
Numerical Study ◽  
Fractal Dimensions ◽  
Series Data ◽  
Geological Features ◽  
The Us ◽  
Good Agreement

Abstract. The time series data of 31 wildfires in 2012 in the US were analyzed. The fractal dimensions (FD) of the wildfires during spreading were studied and their geological features were identified. A growth model based on the cellular automata method is proposed here. Numerical study was performed and is shown to give good agreement with the fractal dimensions and scaling behaviors of the corresponding empirical data.

Chaotic Characteristics of Workface Gas Emission Time-Series Data

2013 ◽  
Vol 340 ◽  
pp. 456-460 ◽  
Author(s):  
Mei Ying Qiao ◽  
Jian Yi Lan
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Chaotic Behavior ◽  
Chaotic Time Series ◽  
Point Of View ◽  
Series Data ◽  
Embedding Dimension ◽  
Minimum Entropy ◽  
Gas Emission ◽  
Emission Time

The chaotic time series phase space reconstruction theory based in this paper. First, the appropriate embedding dimension and delay time are selected by minimum entropy rate. Followed the chaotic behavior are analyzed by the use of the Poincare section map and Power spectrum of time series from the qualitative point of view. Based on NLSR LLE the quantitative study of the chaotic time series characteristics indicators is proposed. Finally, the gas emission workface of Hebi 10th Mine Coal is studied. The several analytical results of the above methods show that: the gas emission time-series data of this workface has chaotic characteristics.

scholarly journals Saline distribution during multicomponent salting in pre-cooked quail eggs

2012 ◽  
Vol 32 (2) ◽  
pp. 281-288 ◽  
Author(s):  
Dionísio Borsato ◽  
Mariete Barbosa Moreira ◽  
Ivanira Moreira ◽  
Marcos Vinicios Roberto Pina ◽  
Rui Sergio dos Santos Ferreira da Silva ◽  
...  
Keyword(s):  
Food Production ◽  
Water System ◽  
Simulated Data ◽  
Comsol Multiphysics ◽  
Predictive Capacity ◽  
Experimental Values ◽  
Relationship Of ◽  
The Mathematical Model ◽  
The Relationship ◽  
Good Agreement

The relationship of NaCl with problems of arterial hypertension has led to a reduction in the levels of this salt in food production. KCl has been used as a partial substitute for NaCl since it cannot be completely substituted without affecting the acceptability of the end product. In this study, the diffusion that occurs during quail egg salting in static and stirred brine was simulated. The mathematical model used was based on a generalization of the Fick's 2nd law, and the COMSOL Multiphysics software was used to simulate the diffusion in the NaCl-KCl-water system. The deviations in the simulated data and experimental data were 2.50% for NaCl and 6.98% for KCl in static brine, while in the stirred brine they were 3.48% for NaCl and 4.72% for KCl. The simulation results presented good agreement with the experimental values and validated the predictive capacity of the model.

PRACTICAL TIME-SERIES ANALYSIS WITH MULTIFRACTAL METHODS

Fractals ◽  
1993 ◽  
Vol 01 (03) ◽  
pp. 735-743 ◽  
Author(s):  
STEPHAN KLEMENT ◽  
KARL W. KRATKY ◽  
JOHANN NITTMANN
Keyword(s):  
Time Series ◽  
Random Number ◽  
Time Series Data ◽  
Chaotic Behavior ◽  
Logistic Map ◽  
Random Number Generator ◽  
Series Data ◽  
Number Generator ◽  
Significant Information ◽  
Particle Count

Time-series data of various origins are studied by analyzing their corresponding multifractal f(α)-spectral which are obtained by use of the so-called canonical method. The classes of data samples under investigation include: (a) airborne particle count data taken from an industrial cleanroom environment; (b) data generated by use of a (pseudo-)random number generator; and (c) data resulting from the iteration of the logistic map for the value r=4.0 of the control parameter, thus exhibiting chaotic behavior. From the resulting multifractal spectra, typical features of the f(α)-curve can be identified in relation to the corresponding class of original data. These findings can be of interest for various purposes. One application under consideration is the processing of microcontamination particle data recorded in high-quality cleanrooms. These are of great importance to the increasing miniaturization of semiconductor devices. In processing microcontamination particle data, the multifractal analysis can help to extract significant information from an enormous number of data to compress these data into a reasonable quantity. Another interesting aspect can be found in using the multifractal spectrum as a possible instrument for estimating the quality and performance of a random number generator.

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