nonlinear regression based algorithm
STANDARD EQUATIONS FOR PREDICTING THE DISCHARGE COEFFICIENT OF A MODIFIED HIGH-PERFORMANCE SIDE WEIR
... PSO algorithm, instead of nonlinear ...calculated based on INPUT1 as the input ...calculated based on INPUT2, has much higher eciency than ...
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A novel multiobjective evolutionary algorithm based on regression analysis
... Modeling method is a crucial part for MOEA because it determines the performance of the algorithms. Zhang et al. built such a model by local principal component analysis (PCA) algorithm [5]. The test results show ...
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Three-structured smooth transition regression models based on CART algorithm
... and Regression Trees) and STR (Smooth Transition ...sition Regression Tree ...parametric nonlinear model through a tree-growing ...transition regression with multiple ...entirely based ...
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Real Time Sentiment Classification of Tweets using Linear (LDA) & Nonlinear (Cart and KNN) Algorithms
... linear algorithm as LDA (Linear Discriminant Analysis) and nonlinear KNN (K Nearest Neighbour) and CART (Classification and Regression Tree) algorithm for classifying the tweets text into ...
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Identification of nonlinear systems with non-persistent excitation using an iterative forward orthogonal least squares regression algorithm
... existing nonlinear system identification methods, the NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous input) model and the associated Orthogonal Forward Regression (OFR) ...
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A Gauss Newton Based Broyden’s Class Algorithm for Parameters of Regression Analysis
... are regression parameters. On the other hand, the regression model is called nonlinear ...many nonlinear regression could be linearization ...
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A Preliminary Performance Study on Nonlinear Regression Models using the Jaya Optimisation Algorithm
... Our performance evaluation of the Jaya optimisation al- gorithm was conducted in two phases. First, we evaluated the performance of Jaya using the RSS as an optimisation criterion for each of the regression ...
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Analysis of Flight Fuel Consumption Based on Nonlinear Regression
... Abstract. For to increase fuel utilization and reduce carbon emissions of flights, the relationship between each factor and fuel consumption was established using real flight data and nonlinear regression ...
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Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems
... is based on the Euclidean norm of the residuals, the new ULS criterion is derived from the Sobolev space ...implement. Based on this, a new Ultra Orthogonal Forward Regression (UOFR) algorithm ...
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A Fast Algorithm for the High Order Linear and Nonlinear Gaussian Regression filter
... Gaussian regression filter, including both the linear and nonlinear filter of zeroth, second order, has been ...fast algorithm based on the FFT algorithm has been proposed and tested ...
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Parallel based support vector regression for empirical modeling of nonlinear chemical process systems
... For the work in this paper, a hybrid approach is proposed. The hyper-parameters are selected by combining the analytical prescriptions proposed in Cherkassky and Ma (2004) and the conventional cross-validation method. ...
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Specification Testing for Nonlinear Cointegrating Regression
... Assumption 1 allows for both a unit root (κ = 0) and a near unit root (κ 6= 0) regressor by virtue of the localizing coefficient κ and is standard in the near integrated regression framework (Phillips, 1987, 1988; ...
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An Evolutionary Algorithm Based on a New Decomposition Scheme for Nonlinear Bilevel Programming Problems
... of nonlinear bilevel programming problems where the follower’s objective is a function of the linear expression of all variables, and the follower’s constraint functions are convex with respect to the follower’s ...
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On concurvity in nonlinear and nonparametric regression models
... is based on the Maximal local correlation statistics (Chen et ...global nonlinear relationship in the input matrix and so to identify the presence of concurvity in a perspective ...
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Influence functions of trimmed likelihood estimators for lifetime experiments
... only demonstrated for trimmed likelihood functionals based on the exponential and the (log)normal distribution in regression models with linear and nonlinear link function. It is possible that it can ...
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Nonlinear regression without i.i.d. assumption
... For overcoming the above difficulty, Lin et al. (2016) studied the linear regression without i.i.d. condition by using the nonlinear expectation framework laid out by Peng (2005). They split the training ...
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How to Orient Yourself in the Wilderness
... There are many papers that determine optimal exercise behavior in insurance con- tracts according to American or Bermudan option pricing techniques (Steffensen, 2002; Tanskanen and Lukkarinen, 2003; Bauer et al., 2008, ...
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Regression Testing Using AIGTCP Algorithm for Industry Based Applications
... ABSTRACT: Regression testing is an important phase in software maintenance activity to ensure the modification caused by ...debugging. Regression testing is a testing to test the modified software during ...
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On the Consistency of a Class of Nonlinear Regression Estimators
... be nonlinear, since a subset of the data is considered, and establishes consistency using two different approaches: (1) using an asymptotic linearity argument and (2) using the uniform law of large numbers of ...
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Hybrid Incremental Model for Imbalance Detection in Ultra-high Precision Rotating Devices
... imbalance based on a hybrid incremental modeling ...–ANFIS, nonlinear regression, and hybrid incremental modeling– in order to evaluate their behavior and ...
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