A nonlinear partial least squares algorithm using quadratic fuzzy inference system

Abdel-Rahman, Araby I.; Lim, Gino J.

doi:10.1002/cem.1249

Cited by 32 publications

(14 citation statements)

References 34 publications

(47 reference statements)

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“…By incorporating non-linear transformation techniques into the PLS framework, non-linear versions of PLS, including Polynomial PLS (PPLS), 18 Spline PLS (SPLS), 19 Quadratic Fuzzy PLS (QFPLS), 20 ANN-NLPLS 21 and kernel PLS 22,23 were developed. These methods can successfully approximate complex non-linear relationships and have the power of PLS to combat over-fitting of linear models in the presence of excessive variability.…”

Section: Non-linear Approachesmentioning

confidence: 99%

Multivariate calibration methods in near infrared spectroscopic analysis

et al. 2010

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Near infrared (NIR) spectroscopy has been demonstrated as a powerful technique for both qualitative and quantitative analysis of complex systems in various fields. Calibration, however, is one of the important techniques needed to ensure the quality and practicability of the analyses. In this minireview, recent developments in multivariate calibration methods for NIR spectroscopic analysis, including non-linear approaches and ensemble techniques, are briefly summarized. The advantages and disadvantages of these methods are compared and discussed critically.

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Section: Non-linear Approachesmentioning

confidence: 99%

Multivariate calibration methods in near infrared spectroscopic analysis

et al. 2010

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“…Although scholars have conducted extensive research into the nonlinear PLS methods, there are still some drawbacks in existing methods. For instance, the spline PLS and artificial neural networks (ANNs) PLS are expected to be appropriate internal mapping function for fitting complex nonlinearity, but the extra flexibility can lead to over fitting and prediction errors [18]. Extreme learning machine (ELM) is a novel single hidden layer feed-forward network with good nonlinear mapping capability [19].…”

Section: Introductionmentioning

confidence: 99%

A Novel Nonlinear Partial Least Square Integrated With Error-Based Extreme Learning Machine

Dong

2019

IEEE Access

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This paper proposes a novel nonlinear partial least square (PLS) approach for dealing with the modeling problem of industrial processes with input variables in collinearity. The new method combines the external linear PLS framework with the internal extreme learning machine (ELM) function. First, PLS is used as the outer framework to extract the input and output latent variables as well as eliminating the collinearity of the original variables, and then ELM is employed to describe the nonlinear relation between pairs of latent variables. Besides, the weight updating strategy based on errors minimization is also involved to improve the prediction accuracy. Then, the pH-neutralization process is taken as a benchmark to verify the validity of the new model. Finally, this method is applied to model the NO x emission of a 1000-MW coal-fired boiler, and root-mean-square error (RMSE) values are 5.9541 for the training dataset and 6.8323 for the testing dataset. Compared with linear PLS and another two nonlinear PLS methods, smaller prediction errors is obtained. The results indicate new nonlinear PLS model can be a better choice for establishing the model of NO x emission for coal-fired boilers. INDEX TERMS Nonlinear partial least square, extreme learn machine, weight update, NO x emission, coal-fired boilers.

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“…KPLS not only retains all of the advantages of PLS but also has strong nonlinear mapping ability. Compared to other nonlinear PLS approaches, such as spline PLS [22], quadratic PLS [23,24], and neural network PLS [25,26], KPLS essentially requires only linear algebra in high-dimensional space, making it as simple as the linear PLS. Moreover, KPLS can handle a wide range of nonlinearities by using different types of kernel functions.…”

Section: Introductionmentioning

confidence: 99%

Safety Monitoring of a Super-High Dam Using Optimal Kernel Partial Least Squares

Huang

Chen

Liu

2015

Mathematical Problems in Engineering

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Considering the characteristics of complex nonlinear and multiple response variables of a super-high dam, kernel partial least squares (KPLS) method, as a strongly nonlinear multivariate analysis method, is introduced into the field of dam safety monitoring for the first time. A universal unified optimization algorithm is designed to select the key parameters of the KPLS method and obtain the optimal kernel partial least squares (OKPLS). Then, OKPLS is used to establish a strongly nonlinear multivariate safety monitoring model to identify the abnormal behavior of a super-high dam via model multivariate fusion diagnosis. An analysis of deformation monitoring data of a super-high arch dam was undertaken as a case study. Compared to the multiple linear regression (MLR), partial least squares (PLS), and KPLS models, the OKPLS model displayed the best fitting accuracy and forecast precision, and the model multivariate fusion diagnosis reduced the number of false alarms compared to the traditional univariate diagnosis. Thus, OKPLS is a promising method in the application of super-high dam safety monitoring.

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A nonlinear partial least squares algorithm using quadratic fuzzy inference system

Cited by 32 publications

References 34 publications

Multivariate calibration methods in near infrared spectroscopic analysis

Multivariate calibration methods in near infrared spectroscopic analysis

A Novel Nonlinear Partial Least Square Integrated With Error-Based Extreme Learning Machine

Safety Monitoring of a Super-High Dam Using Optimal Kernel Partial Least Squares

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