网络多元非线性拟合法 网络释义 1. 多元非线性拟合法 非线性多元函数拟... ... )Multivariate non-linear regression多元非线性拟合法) multi-nonlinear simulating 多元非线性模拟 ... www.dictall.com|基于 1 个网页
This paper proposed a multivariate nonlinear model based on the response surface method with high precision in predicting the specific energy of rock, which could help reduce the specific energy of rock and improve the efficiency of rock breaking....
1.Combining BP neural network withmulti-nonlinear regressionmethod, a frost-resistance grade prediction model, which depends on the main mix parameters of air-entrained concrete, was developed.在充分利用国内外已有的混凝土抗冻试验数据基础上,用人工神经网络与多元非线性回归两种建模方法相结合的途径,建立了能...
2) nonlinear multiple regression analysis 非线性多元回归分析 1. A nonlinear multiple regression analysis method based on feedforword neural network is put forword. 提出了利用前馈神经网络进行非线性多元回归分析的方法 ,该方法能有效解决传统多元回归分析难以解决的非线性多元回归问题 ;并提出了进行非线性...
By letting the predictor variables in MARS be lagged values of a time series, one obtains a new method for nonlinear autoregressive threshold modeling of time series. A significant feature of this extension of MARS is its ability to produce models with limit cycles when modeling time series data...
KPLS is a promising regression method for tackling nonlinear systems because it can efficiently compute regression coefficients in high-dimensional feature spaces by means of nonlinear kernel functions. Unlike other nonlinear partial least squares (PLS) techniques KPLS does not entail any nonlinear ...
Nonlinear interdependency (NLI) analysis is an effective method for measurement of synchronization among brain regions, which is an important feature of no... C Dan,L Wang,C Dong,... - IEEE International Parallel & Distributed Processing Symposium 被引量: 0发表: 2012年 Characterization of hyperte...
On a Projective Resampling Method for Dimension Reduction With Multivariate ResponsesCentral mean subspaceCentral subspaceMonte Carlo integrationMultivariate nonlinear regressionSliced average variance estimatorSliced inverse regressionConsider the dimension reduction problem where both the response and the predictor ...
Ritz and Streibig (2008) provides an overview of nonlinear regression models using R. Loess regression in R provides a flexible nonparametric regression approach to handling up to three inputs. Using generalized additive models (GAM), many more inputs could be accommodated (Wood, 2006). Two ...
MARSplines constructs this relation from a set of coefficients and basis functions that are entirely "driven" from the regression data. In a sense, the method is based on the "divide and conquer" strategy, which partitions the input space into regions, each with its own regression equation. ...