scholarly journals Estimating physiological tolerances - a comparison of traditional approaches to nonlinear regression techniques

2013 ◽  
Vol 216 (12) ◽  
pp. 2176-2182 ◽  
Author(s):  
D. J. Marshall ◽  
M. Bode ◽  
C. R. White
Keyword(s):  
Nonlinear Regression ◽  
Regression Techniques ◽  
Physiological Tolerances ◽  
Traditional Approaches

scholarly journals Different Nonlinear Regression Techniques and Sensitivity Analysis as Tools to Optimize Oil Viscosity Modeling

Resources ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 99
Author(s):  
Dicho Stratiev ◽  
Svetoslav Nenov ◽  
Dimitar Nedanovski ◽  
Ivelina Shishkova ◽  
Rosen Dinkov ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Nonlinear Regression ◽  
Information Criterion ◽  
Least Square ◽  
Gas Oil ◽  
Oil Viscosity ◽  
Prediction Ability ◽  
Regression Techniques ◽  
Comparable Accuracy ◽  
Relative Errors

Four nonlinear regression techniques were explored to model gas oil viscosity on the base of Walther’s empirical equation. With the initial database of 41 primary and secondary vacuum gas oils, four models were developed with a comparable accuracy of viscosity calculation. The Akaike information criterion and Bayesian information criterion selected the least square relative errors (LSRE) model as the best one. The sensitivity analysis with respect to the given data also revealed that the LSRE model is the most stable one with the lowest values of standard deviations of derivatives. Verification of the gas oil viscosity prediction ability was carried out with another set of 43 gas oils showing remarkably better accuracy with the LSRE model. The LSRE was also found to predict better viscosity for the 43 test gas oils relative to the Aboul Seoud and Moharam model and the Kotzakoulakis and George.

Use of nonlinear regression techniques for describing concentration-response relationships of plant species exposed to contaminated site soils

2000 ◽  
Vol 19 (12) ◽  
pp. 2968-2981 ◽  
Author(s):  
Gladys L. Stephenson ◽  
Nicola Koper ◽  
Glenn F. Atkinson ◽  
Keith R. Solomon ◽  
Richard P. Scroggins
Keyword(s):  
Plant Species ◽  
Nonlinear Regression ◽  
Contaminated Site ◽  
Regression Techniques

scholarly journals The regression Tsetlin machine: a novel approach to interpretable nonlinear regression

2019 ◽  
Vol 378 (2164) ◽  
pp. 20190165 ◽  
Author(s):  
K. Darshana Abeyrathna ◽  
Ole-Christoffer Granmo ◽  
Xuan Zhang ◽  
Lei Jiao ◽  
Morten Goodwin
Keyword(s):  
Nonlinear Regression ◽  
Binary Representation ◽  
Propositional Formula ◽  
New Class ◽  
Text Understanding ◽  
Novel Approach ◽  
Continuous Output ◽  
Regression Techniques ◽  
Regression Problems ◽  
Continuous Input

Relying simply on bitwise operators, the recently introduced Tsetlin machine (TM) has provided competitive pattern classification accuracy in several benchmarks, including text understanding. In this paper, we introduce the regression Tsetlin machine (RTM), a new class of TMs designed for continuous input and output, targeting nonlinear regression problems. In all brevity, we convert continuous input into a binary representation based on thresholding, and transform the propositional formula formed by the TM into an aggregated continuous output. Our empirical comparison of the RTM with state-of-the-art regression techniques reveals either superior or on par performance on five datasets. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.

scholarly journals Research of Surrogate Measure for Freeway Crashes Based on Tire Skid Marks

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Yan Ma ◽  
Wenjing Huang ◽  
Zong Tian ◽  
Donghong Li ◽  
Hongzhou Cai ◽  
...  
Keyword(s):  
Sliding Window ◽  
Safety Hazards ◽  
Safety Issues ◽  
Northern Nevada ◽  
Strong Linear Correlation ◽  
Good Potential ◽  
Regression Techniques ◽  
Surrogate Measure ◽  
Traffic Systems ◽  
Traditional Approaches

Traditional approaches to evaluating and predicting safety issues in traffic systems are via crash records. However, considering the characteristics of scarcity, inconsistency, inaccuracy, and incompleteness of crash records, conclusions and recommendations drawn purely based on crashes have limitations. Tire skid marks are considered an indication of some safety hazards, and it could have good potential to be used as surrogates for crashes. By collecting and analyzing the data based on selected arterial and freeway segments in the Reno-Sparks area in northern Nevada, a methodology was developed to categorize different tire skid marks. Sliding window and linear regression techniques were applied to determine any correlation between tire skid marks and crashes. The analyses indicated that there was a relatively strong linear correlation between skid marks and crashes on freeway segments.

Mending and extending observational temperature series by linear and nonlinear regression

2021 ◽  
Author(s):  
Jiří Mikšovský ◽  
Petr Štěpánek
Keyword(s):  
Nonlinear Regression ◽  
Temperature Series ◽  
The Czech Republic ◽  
Improve Accuracy ◽  
Potential Benefits ◽  
Regression Techniques ◽  
Weather Stations ◽  
Temperature Records ◽  
Daily Temperature Series ◽  
Target Locations

<p>While time series of meteorological measurements from land-based weather stations still represent one of the basic types of data employed in climate research, it not uncommon for these records to be incomplete, interrupted by periods of missing or otherwise compromised values. Such gaps typically need to be filled before a subsequent analysis can be performed, and records from other nearby measuring sites are frequently used for this purpose. In this presentation, results of central European daily temperatures estimation from other concurrent measurements by various statistical methods are showcased, with a particular emphasis on assessing potential benefits of application of nonlinear regression techniques. Using multi-decadal daily temperature series originating from a dense network of weather stations covering the territory of the Czech Republic, we show that while nonlinear regression does not always outperform its linear counterpart, it can substantially improve accuracy of temperature estimates for some target locations. The gain is shown to be especially prominent for sites exhibiting atypical behavior compared to their local geographic neighborhood, such as isolated mountain-based stations. In addition to regression-based restoration of compromised segments in the temperature records, use of this methodology for extending the temperature records beyond their original period of measurements is also discussed, as well as its potential for homogeneity testing.</p>

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