• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.394-394
• /
• 2000

This Paper proposes a new nonlinear partial least square method that extends the linear PLS. Proposed nonlinear PLS uses self-organizing feature map as PLS outer relation and multilayer neural network as PLS inner regression method.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.67 no.4
• /
• pp.183-190
• /
• 2018

Predicting accurate electricity prices is an important task in the electricity trading market. To address the electricity price forecasting problem, various approaches have been proposed so far and it is known that linear regression-based approaches are the best. However, the use of such linear regression-based methods is limited due to low accuracy and performance. In traditional linear regression methods, it is not practical to find a nonlinear regression model that explains the training data well. If the training data is complex (i.e., small-sized individual data and large-sized features), it is difficult to find the polynomial function with n terms as the model that fits to the training data. On the other hand, as a linear regression model approximating a nonlinear regression model is used, the accuracy of the model drops considerably because it does not accurately reflect the characteristics of the training data. To cope with this problem, we propose a new electricity price forecasting method that divides the entire dataset to multiple split datasets and find the best linear regression models, each of which is the optimal model in each dataset. Meanwhile, to improve the performance of the proposed method, we modify the proposed localized linear regression method in the map and reduce way that is a framework for parallel processing data stored in a Hadoop distributed file system. Our experimental results show that the proposed model outperforms the existing linear regression model. Specifically, the accuracy of the proposed method is improved by 45% and the performance is faster 5 times than the existing linear regression-based model.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.5
• /
• pp.949-954
• /
• 2009

The autoregressive process is applied in this paper to kernel regression in order to infer nonlinear models for predicting responses. We propose a kernel method for the autoregressive data which estimates the mean function by kernel machines. We also present the model selection method which employs the cross validation techniques for choosing the hyper-parameters which affect the performance of kernel regression. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of mean function in the presence of autocorrelation between data.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.5
• /
• pp.851-856
• /
• 2016

Wind speed is heavily fluctuated and quite local than other weather elements. It is difficult to improve the accuracy of prediction only in a numerical prediction model. An MOS (Model Output Statistics) technique is used to correct the systematic errors of the model using a statistical data analysis. The Most of previous MOS has used a linear regression model for weather prediction, but it is hard to manage an irregular nature of prediction of wind speed. In order to solve the problem, a nonlinear regression method using SVR (Support Vector Regression) is introduced for a development of MOS for wind speed prediction. Experiments are performed for KLAPS (Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea. The MLR and SVR based linear and nonlinear methods are compared to each other for prediction accuracy of wind speed. Also, the comparison experiments are executed for the variation in the number of UM elements.

• Communications for Statistical Applications and Methods
• /
• v.28 no.5
• /
• pp.553-562
• /
• 2021

Directional regression (DR; Li and Wang, 2007) is well-known as an exhaustive sufficient dimension reduction method, and performs well in complex regression models to have linear and nonlinear trends. However, the extension of DR is not well-done upto date, so we will extend DR to accommodate multivariate regression and large p-small n regression. We propose three versions of DR for multivariate regression and discuss how DR is applicable for the latter regression case. Numerical studies confirm that DR is robust to the number of clusters and the choice of hierarchical-clustering or pooled DR.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2000.11a
• /
• pp.6-10
• /
• 2000

In regression model we estimate the unknown parameters using various methods. There are the least squares method which is the most general, the least absolute deviation, the regression quantile and the asymmetric least squares method. In this paper, we will compare each others with two case: to begin with the theoretical comparison in the asymptotic sense, and then the practical comparison using Monte Carlo simulation for a small sample size.

• Structural Engineering and Mechanics
• /
• v.35 no.4
• /
• pp.431-457
• /
• 2010

This article provides a discussion of the mathematic modeling of connections for designing and qualifying structures, systems, and components subject to monotonic or cyclic loading. To characterize the force-deformation behavior of connections under monotonic loading, a review of the Ramberg-Osgood, Richard-Abbott, and Menegotto-Pinto models is conducted, and it is shown that these nonlinear functions can be mathematically derived by scaling up or down a linear force-deformation function. A generalized four-parameter model for simulating connection behavior is investigated to facilitate nonlinear regression analysis. In order to perform seismic analysis of frameworks, a hysteretic model accounting for loading, unloading, and reloading is described using the established monotonic model. For preliminary analysis, a method is provided to quickly determine the model parameters that fit approximately with the observed data. To reach more accurate values of the parameters, the methods of nonlinear regression analysis are investigated and the modified Levenberg-Marquardt and separable nonlinear least-square algorithms are applied in determining the model parameters. Example case studies illustrate the procedure for the computation through the use of experimental/analytical data taken form the literature. Transformation of connection curves from the three-parameter model to the four-parameter model for structural analysis is conducted based on the modeling of connections subject to fire.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.9 no.5
• /
• pp.129-132
• /
• 2009

Frame erasure is one of the most difficult problems in voice over IP (VoIP) networks and is a major source of speech quality degradation. In this paper, a frame erasure concealment algorithm based on nonlinear regression analysis is presented to minimize speech quality deterioration in code-excited linear prediction (CELP) based coders. We applied the proposed scheme to the ITU-T G.729 standard and obtained improved perceptual evaluation of speech quality (PESQ) scores compared to the conventional methods.

• Communications for Statistical Applications and Methods
• /
• v.10 no.3
• /
• pp.719-729
• /
• 2003

We propose a Bayesian model selection procedure for nonlinear regression models under noninformative prior. For informative prior, Na and Kim (2002) suggested the Bayesian model selection procedure through MCMC techniques. We extend this method to the case of noninformative prior. The difficulty with the use of noninformative prior is that it is typically improper and hence is defined only up to arbitrary constant. The methods, such as Intrinsic Bayes Factor(IBF) and Fractional Bayes Factor(FBF), are used as a resolution to the problem. We showed the detailed model selection procedure through the specific real data set.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.1
• /
• pp.71-77
• /
• 2012

This paper introduces the projection methods by nonlinear regression model. To predict the student numbers, a log model and an involution model as the kind of a trend-extrapolation method are used. Empirical evidence shows that a projection by log model is better than by involution model with the confidence interval estimations for the coefficients of determination.