Stability analysis and control design of LTI discrete-time systems by the direct use of time series data

Automatica ◽  
2009 ◽  
Vol 45 (5) ◽  
pp. 1265-1271 ◽  
Author(s):  
Un Sik Park ◽  
Masao Ikeda
Keyword(s):  
Time Series ◽  
Stability Analysis ◽  
Time Series Data ◽  
Control Design ◽  
Series Data ◽  
Use Of Time ◽  
Discrete Time Systems ◽  
And Control ◽  
Time Systems ◽  
Direct Use

Switching Control Design for Switched Fuzzy Discrete-Time Systems

2014 ◽  
Vol 1006-1007 ◽  
pp. 711-714
Author(s):  
Hong Yang ◽  
Huan Huan Lü ◽  
Le Zhang
Keyword(s):  
Stability Analysis ◽  
Discrete Time ◽  
Fuzzy Systems ◽  
Closed Loop ◽  
Control Method ◽  
Control Design ◽  
Switching Control ◽  
Discrete Time Systems ◽  
The Stability ◽  
Time Systems

This paper investigates the problems of stability analysis and stabilization for a class of switched fuzzy discrete-time systems. Based on a common Lyapunov functional, a switching control method has been developed for the stability analysis of switched discrete-time fuzzy systems. A new stabilization approach based on a switching parallel distributed compensation scheme is given for the closed-loop switched fuzzy systems. Finally, the illustrative example is provided to demonstrate the effectiveness of the techniques proposed in this paper.

scholarly journals Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network

2017 ◽  
Vol 145 (6) ◽  
pp. 1118-1129 ◽  
Author(s):  
K. W. WANG ◽  
C. DENG ◽  
J. P. LI ◽  
Y. Y. ZHANG ◽  
X. Y. LI ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Prevention And Control ◽  
Time Series Data ◽  
Arima Model ◽  
Public Health Problem ◽  
Series Data ◽  
Percentage Error ◽  
And Control

SUMMARYTuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

scholarly journals Estimating Elasticities for Multi-Modal Public Transport Demand: A Time Series Approach

2010 ◽  
Author(s):  
Konstantina Gkritza ◽  
Ioannis Golias ◽  
Matthew G. Karlaftis
Keyword(s):  
Time Series ◽  
Public Transport ◽  
Public Transportation ◽  
Time Series Data ◽  
Transportation Systems ◽  
Seemingly Unrelated Regression ◽  
Series Data ◽  
Factors Affecting ◽  
Use Of Time ◽  
Time Series Approach

Research on the demand side of public transportation systems with the use of time series data frequently shows conflicting results with respect to fare elasticities and the factors affecting it. In this analysis we complement prior research by developing seemingly unrelated regression equation models with monthly data for a city served by three different modes of public transportation. The results indicate that, as expected, urban public transport demand in Athens, Greece, is inelastic with respect to fares but, surprisingly, highly inelastic with respect to automobile fuel cost. Further, different transit modes have significantly different fare elasticities, a finding with important practical implications.

Detection of Thermoacoustic Instabilities Via Nonparametric Bayesian Markov Modeling of Time-Series Data

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Sihan Xiong ◽  
Sudeepta Mondal ◽  
Asok Ray
Keyword(s):  
Time Series ◽  
Real Time ◽  
Time Series Data ◽  
Data Driven ◽  
Series Data ◽  
Markov Modeling ◽  
Bayesian Nonparametric ◽  
Dynamic Data ◽  
Thermoacoustic Instabilities ◽  
And Control

Real-time detection and decision and control of thermoacoustic instabilities in confined combustors are challenging tasks due to the fast dynamics of the underlying physical process. The objective here is to develop a dynamic data-driven algorithm for detecting the onset of instabilities with short-length time-series data, acquired by available sensors (e.g., pressure and chemiluminescence), which will provide sufficient lead time for active decision and control. To this end, this paper proposes a Bayesian nonparametric method of Markov modeling for real-time detection of thermoacoustic instabilities in gas turbine engines; the underlying algorithms are formulated in the symbolic domain and the resulting patterns are constructed from symbolized pressure measurements as probabilistic finite state automata (PFSA). These PFSA models are built upon the framework of a (low-order) finite-memory Markov model, called the D-Markov machine, where a Bayesian nonparametric structure is adopted for: (i) automated selection of parameters in D-Markov machines and (ii) online sequential testing to provide dynamic data-driven and coherent statistical analyses of combustion instability phenomena without solely relying on computationally intensive (physics-based) models of combustion dynamics. The proposed method has been validated on an ensemble of pressure time series from a laboratory-scale combustion apparatus. The results of instability prediction have been compared with those of other existing techniques.

scholarly journals Do banking sector reforms cause economic growth?: Empirical evidence from Africa’s largest economy

2015 ◽  
Vol 13 (1) ◽  
pp. 553-564
Author(s):  
Andy Titus Okwu ◽  
Olusola Babatunde Falaiye ◽  
Rowland Tochukwu Obiakor ◽  
Ajibola Joseph Olusegun
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Banking Sector ◽  
Time Series Data ◽  
Series Data ◽  
Real Sector ◽  
Reform Strategies ◽  
Long Run ◽  
Short Run ◽  
And Control

This paper employed time series data on relevant empirical diagnostics to examine banking sector growth-led nexus within the context of Africa’s largest economy, Nigeria. Diagnostics established stationarity of banking sector indicators and control variables at first difference. Findings showed no causal relationships between banking sector reforms and economic growth in the short-run and that, though liberalisation in particular did not Granger-cause growth of the economy during the study period, banking sector reforms caused growth of the real sector of the Nigerian economy. Hence, the caveat was that long-run growth effects of banking sector reforms on real sectors of economies are functions of policy targets of such banking or financial sectors reform strategies. Consequently, articulation of banking and financial sectors reforms within long-run rather than short-run perspectives and complementarity of liberalisation were recommended.

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