• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.211-218
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• 2007

Nonlinear ARMA model $X_n\;=\;h(X_{n-1},{\cdots},X_{n-p},e_{n-1},{\cdots},e_{n-p})+e_n$ is considered and easy-to-check sufficient condition for strict stationarity of {$X_n$} without some irreducibility or continuity assumption is given. Threshold ARMA(p, q) and momentum threshold ARMA(p, q) models are examined as special cases.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2003.05a
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• pp.240-243
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• 2003

Combustion instability phenomena have been observed in various different combustion systems. For each specific combustion system, pressure fluctuations measured during high frequency combustion instability presented many different characteristics. High frequency instability occurring in a lean premixed gas turbine combustor mar be dominantly affected by a nonlinear relation between pressure oscillations and heat release rate fluctuations, and gas dynamics plays a crucial role in determining an amplitude of a limit cycle for a liquid rocket thrust chamber. Combustion instability phenomena manifest their inherent nonlinear characteristics. One is a limit cycle and the other bifurcation described by nonlinear time series analysis.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.861-864
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• 1993

This research provides the results of a trial to generate the chaos by using nonlinear function constructed by fuzzy inference rules. The chaos generation function or chaotic behavior can be obtained by using Takagi-Sugeno fuzzy model with some constraint of the relationship of its parameters. Two examples are shown in this research. The first is simple example that construct of logistic image by fuzzy model. The second is more complicated one that provide the chaotic time series by non-linear autoregression based on fuzzy model. Simulated results are shown in these examples.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1389-1389
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• 2003

• Journal of the Korean Society for Precision Engineering
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• v.12 no.6
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• pp.145-151
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• 1995

The first-order Taylor series expansion can be evaluated analytically from the formulated symbolic nonlinear dynamic equations. A closed-form linear dynamic euation is derived about a nominal trajectory. The state space representation of the linearized dynamics can be derived easily from the closed-form linear dynamic equations. But manual symbolic expansion of dynamic equations and linearization is tedious, time-consuming and error-prone. So it is desirable to manipulate the procedures using a computer. In this paper, the analytic linearization is performed using the symbolic language MATHEMATICA. Two examples are given to illustrate the approach anbd to compare nonlinear model with linear model.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.262-269
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• 2016

In this paper, we propose a novel dynamical love model of Romeo and Juliet, which has an external force with a fuzzy membership function. The external force used in the model has the characteristics of a Gaussian function. The chaotic behavior in the model is demonstrated using time series and phase portraits.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.534-538
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• 1987

State-space techniques are employed to derive an equivalent nonlinear recurrent time-domain model that describes the series resonant converter behavior exactly. This model is employed effectively to analyze large signal behavior by propagating the recurrent equation and matching boundary conditions through digital computation. The model is verified with a laboratory converter for a steady-state operation.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.821-830
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• 2006

This paper develops support vector based fuzzy linear and nonlinear regression models and applies it to forecasting the exchange rate. We use the result of Tanaka(1982, 1987) for crisp input and output. The model makes it possible to forecast the best and worst possible situation based on fewer than 50 observations. We show that the developed model is good through real data.

• Journal of applied mathematics & informatics
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• v.27 no.1_2
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• pp.315-326
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• 2009

The unknown parameters in regression models are usually estimated by using various existing methods. There are several existing methods, such as the least squares method, which is the most common one, the least absolute deviation method, the regression quantile method, and the asymmetric least squares method. For the nonlinear time series regression models, which do not satisfy the general conditions, we will compare them in two ways: 1) a theoretical comparison in the asymptotic sense and 2) an empirical comparison using Monte Carlo simulation for a small sample size.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1147-1150
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• 2008

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of the support vector machines neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The SVM-NNM in time series modeling is relatively new and it is more problematic in comparison with classifications. In this study, The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training, cross validation, and testing data, respectively. From this research, we evaluate the impact of the SVM-NNM and the MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.