complex nonlinear least‐squares regressiondata qualityexcessive bias errorsill‐conditioned regressionimpedance spectroscopylinear regression formalismregression qualitystochastic errorsThis chapter provides an overview of issues associated with regression. The regression of models to impedance data generally employs ...
Ding, Y. Jia, Adaptive nonlinear PCA algorithms for blind source separation without prewhitening. IEEE Trans. Circuits Syst.-Regular Paper. 53(3), 745–753 (2006) Download references Acknowledgements This work was supported by the Guangxi Natural Science Foundation under Grant No. 2023GXNSFBA...
Optimal blind nonlinear least-squares carrier phase and frequency offset estimation for general QAM modulations A practical implementation of the optimal matched estimator, which is a computationally efficient approximation of the latter and exhibits negligible performance ... W Yan,E Serpedin,P Ciblat ...
There must be no constraints, not even bounds. Complex numbers are not well ordered, so it is not clear what “bounds” might mean. When there are problem bounds, nonlinear least-squares solvers disallow steps leading to complex values. ...
We use these complex Taylor series expansions to generalize existing optimization algorithms for both general nonlinear optimization problems and nonlinear least squares problems. We then apply these methods to two case studies which demonstrate that complex derivatives can lead to greater insight in the ...
In view of the above issues in process monitoring, a novel simple yet robust process monitoring model for the complex nonlinear process industry based on a kind of denoising sparse auto-encoder (DSAE) is presented in this article. Specifically, the DSAE is a hybrid auto-encoder established by...
This strategy, in contrast to Support Vector Machines technique, shows the conceptual simplicity of least mean square algoritm for linear regression but allows local nonlinear aproximation of the system evolution, with low computational cost.关键词: Theoretical or Mathematical/ chaos Haar transforms ...
Nonlinear least-square regression with the Gauss–Newton algorithm was applied to estimate the parameters in Eq. (1). In order to identify the bounds of parameters search, this method needs starting points to be determined, which were\({p_{i}} = 0.007\)\({q_{i}}\)= 0.09 for Eq. (...
concentration of ZZ,pboundis the fraction of protein bound to a ligand, andPC1is the normalized principal component, obtained by TREND, that indicates the change in the population of the bound state. The errors of theKdvalue are the fitting uncertainties from nonlinear least-squares fits in ...
Meshless membrane model based on the moving least-squares method A meshless particle-based membrane model is proposed. The particles possess no internal degree of freedom and interact via a potential, which has three dif... H Noguchi,G Gompper - 《Physical Review E Statistical Nonlinear & Soft...