• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2007년도 추계학술발표대회
• /
• pp.113-116
• /
• 2007

In this paper, our proposed Nonlinear Autoregressive Exogenous (NARX) based on Least Square-Support Vector Regression (LS-SVR) is introduced and tested for producing natural sounds. This nonlinear synthesizer perfectly reproduce voiced sounds, and also conserve the naturalness such as jitter and shimmer, compared to LPC does not keep these naturalness. However, the results of some phonation are quite different from the original sounds. These results are assumed that single-band model can not afford to control and decompose the high frequency components. Therefore multi-band model with wavelet filterbank is adopted for substituting single band model. As a results, multi-band model results in improved stability. Finally, nonlinear speech modeling using NARX based on LS-SVR can successfully reconstruct synthesized sounds nearly similar to original voiced sounds.

• Journal of the Korean Data and Information Science Society
• /
• 제28권5호
• /
• pp.971-980
• /
• 2017

가변계수 회귀모형은 회귀계수의 동적변화를 모형화함으로써 종속변수와 입력변수의 관계에 대한 쉬운 해석이 가능하고 회귀계수의 변동성도 추정할 수 있는 장점을 지니고 있으므로, 여러 과학 분야에서 많은 주목을 받고 있다. 본 논문에서 입력변수와 출력변수의 오차를 효과적으로 고려한 가변계수 오차모형을 제안한다. 가변계수가 평활변수의 알려지지 않은 형태의 비선형함수이므로 이를 추정하기 위하여 커널 방법을 사용한다. 제안된 모형의 성능에 영향을 미치는 초모수의 최적값을 구하기 위하여 일반화 교차타당성 방법 또한 제안한다. 제안된 방법은 모의자료와 실제자료를 이용한 수치적 연구를 통하여 평가된다.

• 대한의용생체공학회:의공학회지
• /
• 제22권4호
• /
• pp.369-375
• /
• 2001

The statistical regression model is one of the most frequently used clinical analysis methods. It has basic assumption of linearity, additivity and normal distribution of data. However, most of biological data in medical field are nonlinear and unevenly distributed. To overcome the discrepancy between the basic assumption of statistical model and actual biological data, we propose a new analytical method based on artificial neural network. The newly developed multilayer perceptron(MLP) is trained with 120 data set (60 normal, 60 patient). On applying test data, it shows the discrimination power of 0.76. The diabetic risk factors were also identified from the MLP neural network model and the logistic regression model. The signigicant risk factors identified by MLP model were post prandial glucose level(PP2), sex(male), fasting blood sugar(FBS) level, age, SBP, AC and WHR. Those from the regression model are sex(male), PP2, age and FBS. The combined risk factors can be identified using the MLP model. Those are total cholesterol and body weight, which is consistent with the result of other clinical studies. From this experiment we have learned that MLP can be applied to the combined risk factor analysis of biological data which can not be provided by the conventional statistical method.

• 한국철도학회:학술대회논문집
• /
• 한국철도학회 2001년도 추계학술대회 논문집
• /
• pp.475-480
• /
• 2001

In this study, a empirical equation which can be feasibly used to evaluate minimal track buckling strength without exact numerical analysis is presented. Parameter studies we carried out to investigate the effects of the individual factor on buckling strength. In order to simulate track buckling in the field as precisely as possible, a rigorous buckling model which accounts for all the important parameters is adopted. A empirical equation for prediction of minimal track buckling strength is derived by taking nonlinear regression of data which are obtained from numerical analyses. Its characteristics and applicability are investigated by comparing the results by the presented equation with the one by the equation which was presented in japan, and is frequently using in korea when designing track structure.

• 대한수의학회지
• /
• 제53권2호
• /
• pp.73-76
• /
• 2013

The aim of this study was to produce a simple and inexpensive technique for estimating the obturator foramen area (OFA) from young calves based on the hypothesis that OFA can be extrapolated from simple linear measurements. Three linear measurements - dorsoventral height, craneocaudal width and total perimeter of obturator foramen - were obtained from 55 bovine hemicoxae. Different algorithms for determining OFA were then produced with a regression analysis (curve fitting) and statistical analysis software. The most simple equation was OFA ($mm^2$) = [3,150.538 + ($36.111^*CW$)] - [147,856.033/DH] (where CW = craneocaudal width and DH = dorsoventral height, both in mm), representing a good nonlinear model with a standard deviation of error for the estimate of 232.44 and a coefficient of multiple determination of 0.846. This formula may be helpful as a repeatable and easily performed estimation of the obturator foramen area in young bovines. The area of the obturator foramen magnum can thus be estimated using this regression formula.

• 응용통계연구
• /
• 제6권1호
• /
• pp.163-172
• /
• 1993

기존의 몇몇 통계 ？키지들(SAS, SPSS)에서 제공하는 비선형 회귀분석을 위한 프로그램들은 그 사용방법이 불편하고, 최근에 개발된 중요한 통계치는 계산을 해주지 못하고 있다. 여기서 소개하고자 하는 비선형 회귀분석을 위한 소프트웨어 NLIN90은 비선형 회귀모형들의 데이터베이스화로 사용이 편리하고, 기본 통계치외에 모형 전체와 모수 개개에 대한 곡률분석, 변환된 모수에 관한 곡률분석, 실험계획분석등 정확한 분석에 필요한 많은 통계치를 제공하는 소프트웨어로 개발되었다.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2001년도 학술발표회 논문집(I)
• /
• pp.45-51
• /
• 2001

The daily inflow is apparently one of nonlinear and complicated phenomena. The nonlinear and complexity make it difficult to model the prediction of daily flow, but attractive to try the neural networks approach which contains inherently nonlinear schemes. The study focuses on developing the forecasting models of daily inflows to a large dam site using neural networks. In order to reduce the error caused by high or low outliers, the back propagation algorithm which is one of neural network structures is modified by combining a regression algorithm. The study indicates that continuous forecasting of a reservoir inflow in real time is possible through the use of modified neural network models. The positive effect of the modification using tole regression scheme in BP algorithm is showed in the low and high ends of inflows.

• Communications for Statistical Applications and Methods
• /
• 제19권2호
• /
• pp.293-301
• /
• 2012

Quantile regression proposed by Koenker and Bassett (1978) is a statistical technique that estimates conditional quantiles. The advantage of using quantile regression is the robustness in response to large outliers compared to ordinary least squares(OLS) regression. A regression tree approach has been applied to OLS problems to fit flexible models. Loh (2002) proposed the GUIDE algorithm that has a negligible selection bias and relatively low computational cost. Quantile regression can be regarded as an analogue of OLS, therefore it can also be applied to GUIDE regression tree method. Chaudhuri and Loh (2002) proposed a nonparametric quantile regression method that blends key features of piecewise polynomial quantile regression and tree-structured regression based on adaptive recursive partitioning. Lee and Lee (2006) investigated wage determinants in the Korean labor market using the Korean Labor and Income Panel Study(KLIPS). Following Lee and Lee, we fit three kinds of quantile regression tree models to KLIPS data with respect to the quantiles, 0.05, 0.2, 0.5, 0.8, and 0.95. Among the three models, multiple linear piecewise quantile regression model forms the shortest tree structure, while the piecewise constant quantile regression model has a deeper tree structure with more terminal nodes in general. Age, gender, marriage status, and education seem to be the determinants of the wage level throughout the quantiles; in addition, education experience appears as the important determinant of the wage level in the highly paid group.

• Management Science and Financial Engineering
• /
• 제14권2호
• /
• pp.89-104
• /
• 2008

Growth curves including Bass, Logistic and Gompertz functions are widely used in forecasting the market demand. Nonlinear least square method is often adopted for estimating the model parameters but it is difficult to set up the starting value for each parameter. If a wrong starting point is selected, the result may lead to erroneous forecasts. This paper proposes a method of selecting starting values for model parameters in estimating some growth curves by nonlinear least square method through grid search and transformation into linear regression model. Resealing the market data using the national economic index makes it possible to figure out the range of parameters and to utilize the grid search method. Application to some real data is also included, where the performance of our method is demonstrated.

• The Plant Pathology Journal
• /
• 제26권1호
• /
• pp.17-24
• /
• 2010

A logistic model for describing combined effects of both temperature and wetness period on appressorium formation was developed using laboratory data on percent appressorium formation of Colletotrichum acutatum. In addition, the possible use of the logistic model for forecasting infection risks was also evaluated as compared with a first-order linear model. A simplified equilibrium model for enzymatic reactions was applied to obtain a temperature function for asymptote parameter (A) of logistic model. For the position (B) and the rate (k) parameters, a reciprocal model was used to calculate the respective temperature functions. The nonlinear logistic model described successfully the response of appressorium formation to the combined effects of temperature and wetness period. Especially the temperature function for asymptote parameter A reflected the response of upper limit of appressorium formation to temperature, which showed the typical temperature response of enzymatic reactions in the cells. By having both temperature and wetness period as independent variables, the nonlinear logistic model can be used to determine the length of wetness periods required for certain levels of appressorium formation under different temperature conditions. The infection model derived from the nonlinear logistic model can be used to calculate infection risks using hourly temperature and wetness period data monitored by automated weather stations in the fields. Compared with the nonlinear infection model, the linear infection model always predicted a shorter wetness period for appressorium formation, and resulted in significantly under- and over-estimation of response at low and high temperatures, respectively.