Applications of Linear and Nonlinear Regression Equations for Engineering

2007 ◽  
pp. 1406-1419
2010 ◽  
Vol 47 (6) ◽  
pp. 30-39
Author(s):  
D. Blumberga ◽  
I. Veidenbergs ◽  
J. Gusca ◽  
M. Rosa

Evaluation of CO2Emissions from Energy Sources in LatviaThe authors propose an empirical model for evaluation of the CO2emissions released from the energy generation sources as a function of the fuel types, energy efficiency of the technologies used, and the emission factors of the fuels. In the research, multifactor linear and nonlinear regression equations are employed. The developed model has been proved with the data of the energy generation sources taking part in the Latvian Emission Trading Scheme.


Transport ◽  
2002 ◽  
Vol 17 (6) ◽  
pp. 219-222
Author(s):  
Mindaugas Mazūra ◽  
Olga Fadina

Major problems of forecasting the economic characteristics of transportation (i.e. the amount of freight and passengers carried, the turnover rate of freight and passengers, etc. in transportation as a whole and in particular areas using various transport facilities) are demonstrated. Methods for predicting the development of transportation based on multidimensional regression and correlation analysis and realizing mathematical models for choosing linear and nonlinear regression equations, more accurately approximating the empirical data, are presented. The research conducted has demonstrated that the most reliable forecasts may be made when the methods of choosing the proper non-linear regression equation described in Section 2 of the present paper are used.


1992 ◽  
Vol 57 (10) ◽  
pp. 2053-2058
Author(s):  
Václav Dušek ◽  
František Skopal

Linear and nonlinear regression methods are compared with respect to their application to the evaluation of chemico-kinetic measurements of a feedback reactor. Their assets and pitfalls are demonstrated.


2017 ◽  
Vol 110 (3) ◽  
pp. 302-309 ◽  
Author(s):  
David A. Ratkowsky ◽  
Gadi V. P. Reddy

Abstract Previous empirical models for describing the temperature-dependent development rates for insects include the Briére, Lactin, Beta, and Ratkowsky models. Another nonlinear regression model, not previously considered in population entomology, is the Lobry–Rosso–Flandrois model, the shape of which is very close to that of the Ratkowsky model in the suboptimal temperature range, but which has the added advantage that all four of its parameters have biological meaning. A consequence of this is that initial parameter estimates, needed for solving the nonlinear regression equations, are very easy to obtain. In addition, the model has excellent statistical properties, with the estimators of the parameters being “close-to-linear,” which means that the least squares estimators are close to being unbiased, normally distributed, minimum variance estimators. The model describes the pooled development rates very well throughout the entire biokinetic temperature range and deserves to become the empirical model of general use in this area.


Author(s):  
Jiansheng Wu

Rainfall forecasting is an important research topic in disaster prevention and reduction. The characteristic of rainfall involves a rather complex systematic dynamics under the influence of different meteorological factors, including linear and nonlinear pattern. Recently, many approaches to improve forecasting accuracy have been introduced. Artificial neural network (ANN), which performs a nonlinear mapping between inputs and outputs, has played a crucial role in forecasting rainfall data. In this paper, an effective hybrid semi-parametric regression ensemble (SRE) model is presented for rainfall forecasting. In this model, three linear regression models are used to capture rainfall linear characteristics and three nonlinear regression models based on ANN are able to capture rainfall nonlinear characteristics. The semi-parametric regression is used for ensemble model based on the principal component analysis technique. Empirical results reveal that the prediction using the SRE model is generally better than those obtained using other models in terms of the same evaluation measurements. The SRE model proposed in this paper can be used as a promising alternative forecasting tool for rainfall to achieve greater forecasting accuracy and improve prediction quality.


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