Nonlinear regression ≈ Nonlinear regression
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Modelling and Interpretation of Adsorption Isotherms Open
The need to design low-cost adsorbents for the detoxification of industrial effluents has been a growing concern for most environmental researchers. So modelling of experimental data from adsorption processes is a very important means of p…
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A Toolbox for Nonlinear Regression in<i>R</i>: The Package<b>nlstools</b> Open
Nonlinear regression models are applied in a broad variety of scientific fields. Various R functions are already dedicated to fitting such models, among which the function nls() has a prominent position. Unlike linear regression fitting of…
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Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran Open
Evaporation accounts for varying shares of water balance under different climatic conditions, and its correct prediction poses a significant challenge before water resources management in watersheds. Given the complex and nonlinear behavio…
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Constrained sparse Galerkin regression Open
The sparse identification of nonlinear dynamics (SINDy) is a recently proposed data-driven modelling framework that uses sparse regression techniques to identify nonlinear low-order models. With the goal of low-order models of a fluid flow…
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Nonlinear regression analysis of the sorption of crystal violet and methylene blue from aqueous solutions onto an agro-waste derived activated carbon Open
Sorption of synthetic dyes on low-cost solid sorbents is a simple technique for their removal from wastewater. Recent initiatives in the sorption process have sought the use of activated carbon derived from agricultural wastes as it provid…
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On validity, physical meaning, mechanism insights and regression of adsorption kinetic models Open
The study of adsorption kinetics is ubiquitous when reporting new adsorbent materials in the literature. The information these tests provide are no doubt valuable, but the conclusions drawn from adsorption kinetic models are many times con…
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A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications Open
The ridge regression-type (Hoerl and Kennard, 1970) and Liu-type (Liu, 1993) estimators are consistently attractive shrinkage methods to reduce the effects of multicollinearity for both linear and nonlinear regression models. This paper pr…
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Modelling the strength of lightweight foamed concrete using support vector machine (SVM) Open
Strength of concrete is a primary criterion in selecting this material for a particular application. This construction material gains strength over a long period of time after pouring. Characteristic strength of normal concrete that consid…
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Functional additive regression Open
We suggest a new method, called Functional Additive Regression, or FAR, for\nefficiently performing high-dimensional functional regression. FAR extends the\nusual linear regression model involving a functional predictor, $X(t)$, and a\nsca…
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Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model Open
This research carries out a comparative study to investigate a machine learning solution that employs the Gaussian Process Regression (GPR) for modeling compressive strength of high-performance concrete (HPC). This machine learning approac…
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Parameter identification for symbolic regression using nonlinear least squares Open
In this paper we analyze the effects of using nonlinear least squares for parameter identification of symbolic regression models and integrate it as local search mechanism in tree-based genetic programming. We employ the Levenberg–Marquard…
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Deep Residual Learning for Nonlinear Regression Open
Deep learning plays a key role in the recent developments of machine learning. This paper develops a deep residual neural network (ResNet) for the regression of nonlinear functions. Convolutional layers and pooling layers are replaced by f…
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Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity Open
Hydraulic conductivity of soil reveals its influencing role in the studies related to management of surface and subsurface flow, e.g. irrigation and drainage projects, and solute mass transport models. Direct measurements of hydraulic cond…
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Quantifying cardinal temperatures and thermal time required for germination of Silybum marianum seed Open
The response of seed germination to environmental factors can be estimated by nonlinear regression. The present study was performed to compare four nonlinear regression models (segmented, beta, beta modified, and dent-like) to describe the…
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Nonlinear regression for treating adsorption isotherm data to characterize new sorbents: Advantages over linearization demonstrated with simulated and experimental data Open
This paper demonstrates that determining adsorption capacity and affinity through data fitting of adsorption isotherms by nonlinear regression (NLR) is more accurate than linearized Langmuir equations. Linearization errors and the subjecti…
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Regression analysis of major parameters affecting the intensity of coal and gas outbursts in laboratory Open
Estimating the intensity of outbursts of coal and gas is important as the intensity and frequency of outbursts of coal and gas tend to increase in deep mining. Fully understanding the major factors contributing to coal and gas outbursts is…
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Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm Open
This paper analyses the effectiveness of determining gas concentrations by using a prototype WO 3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition …
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Optimal sup-norm rates and uniform inference on nonlinear functionals of nonparametric IV regression Open
This paper makes several important contributions to the literature about nonparametric instrumental variables (NPIV) estimation and inference on a structural function h0 and its functionals. First, we derive sup-norm convergence rates for …
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How entropic regression beats the outliers problem in nonlinear system identification Open
In this work, we developed a nonlinear System Identification (SID) method that we called Entropic Regression. Our method adopts an information-theoretic measure for the data-driven discovery of the underlying dynamics. Our method shows rob…
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Estimating nonlinear effects of fiscal policy using quantile regression methods Open
We use quantile regression methods to estimate the effects of government spending shocks on output and unemployment rates. This allows to uncover nonlinear effects of fiscal policy by letting the parameters of either vector autoregressive …
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Double sigmoidal models describing the growth of coffee berries Open
This study aimed to verify if the growth pattern of coffee berries, considering fresh mass accumulation over time, is double sigmoid and to select the most suitable nonlinear model to describe such behavior. Data used consisted of fourteen…
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Prediction of Electrical Output Power of Combined Cycle Power Plant Using Regression ANN Model Open
Recently, regression artificial neural networks are used to model various systems that have high dimensionality with nonlinear relations. The system under study must have enough dataset available to train the neural network. The aim of thi…
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Sparse Heteroscedastic Multiple Spline Regression Models for Wind Turbine Power Curve Modeling Open
An accurate wind turbine power curve (WTPC) plays a vital role in wind power forecasting and wind turbine condition monitoring. There are two major shortcomings of current WTPC models that prevent more accurate WTPC estimation, limited non…
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Identifying short- and long-time modes of the mean-square displacement: An improved nonlinear fitting approach Open
This paper is concerned with fitting the mean-square displacement (MSD) function, and extract reliable and accurate values for the diffusion coefficient D. In this work, we present a new optimal and robust nonlinear regression model capabl…
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Comparative assessments of binned and support vector regression-based blade pitch curve of a wind turbine for the purpose of condition monitoring Open
The unexpected failure of wind turbine components leads to significant downtime and loss of revenue. To prevent this, supervisory control and data acquisition (SCADA) based condition monitoring is considered as a cost-effective approach. I…
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Linear and nonlinear kinetics analysis and adsorption characteristics of packed bed column for phenol removal using rice husk-activated carbon Open
The linear and nonlinear kinetics analysis and adsorption characteristics of phenol adsorption onto activated carbon synthesized from rice husk biomass were investigated in a packed bed column. Several analyses such as physical properties,…
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Month ahead average daily electricity price profile forecasting based on a hybrid nonlinear regression and SVM model: an ERCOT case study Open
With the deregulation of the electric power industry, electricity price forecasting plays an increasingly important role in electricity markets, especially for retailors and investment decision making. Month ahead average daily electricity…
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Mechanical Properties of 3D-Printed Components Using Fused Deposition Modeling: Optimization Using the Desirability Approach and Machine Learning Regressor Open
The fused deposition modelling (FDM) technique involves the deposition of a fused layer of material according to the geometry designed in the software. Several parameters affect the quality of parts produced by FDM. This paper investigates…
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Mathematical Modeling of Thin Layer Drying Kinetics and Moisture Diffusivity Study of Pretreated Moringa oleifera Leaves Using Fluidized Bed Dryer Open
Investigations were undertaken to study the drying kinetics of pretreated and unblanched leaves of Moringa oleifera dried in a fluidized bed dryer (FBD) using nine established thin layer drying mathematical models. The statistical software…
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An Empirical Comparison of Multiple Linear Regression and Artificial Neural Network for Concrete Dam Deformation Modelling Open
Deformation predicting models are essential for evaluating the health status of concrete dams. Nevertheless, the application of the conventional multiple linear regression model has been limited due to the particular structure, random load…