A nonlinear partial least squares with slice transform based piecewise linear inner relation

Shan, Peng; Peng, Silong; Tang, Liang; Yang, Chixiao; Zhao, Yuhui; Xie, Qun; Li, Changwen

doi:10.1016/j.chemolab.2015.02.015

Cited by 18 publications

(18 citation statements)

References 30 publications

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“…In the last few years, PLS has been described in books and in tutorials [25][26][27][28][29][30][31][32]. Currently, PLS is receiving increased attention in the literature, for instance, during the last year or so, several papers describing the application of PLS to chemical problems have been published [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49].…”

Section: Pls and Anns For Spectral Interference Correctionmentioning

confidence: 99%

Artificial neural networks (ANNS) versus partial least squares (PLS) for spectral interference correction for taking part of the lab to the sample types of applications: an experimental study

Karanassios

2016

SPIE Proceedings

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Interference and in particular spectral interference is a well documented problem in optical emission spectrometry. For example, it is commonly encountered even when commercially-available spectrometers with medium to high resolution are used (e.g., those with focal lengths of 0.75 m to 1 m). Such interference must be corrected. Although portable spectrometers are better suited for "taking part of the lab to the sample" types of applications, the effects of interference become more pronounced due to the short focal length of such spectrometers (e.g., 10 cm to 15 cm). We describe use of Artificial Neural Networks (ANNs) and of Partial Least Squares (PLS) methods for spectral interference correction.

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Section: Pls and Anns For Spectral Interference Correctionmentioning

confidence: 99%

Artificial neural networks (ANNS) versus partial least squares (PLS) for spectral interference correction for taking part of the lab to the sample types of applications: an experimental study

Karanassios

2016

SPIE Proceedings

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“…Inner nonlinear PLS model is where the internal linear model between latent variables is replaced by a nonlinear model, but its external model remains unchanged, such as quadratic partial least squares (QPLS) (Wold et al 1989), spline function PLS (SPLS) (Wold 1992), and neural network PLS (NNPLS) McAvoy 1992, 1996) approaches. Recursive nonlinear PLS (RNPLS) models are built by extending the input and output matrices on top of PLS (Li et al 2005); nonlinear PLS (NPLSSLT) based on the slice transformation (SLT) can be used for nonlinear correction, where SLT-based segmented linear mapping functions are used to construct nonlinear relationships between input and output score vectors (Shan et al 2015); and nonlinear iterative partial least square algorithm (NIPALS) is improved by assuming that the score vector is a linear projection of the original variables in the internal nonlinear PLS, at the cost of increased computational complexity and optimization complexity.…”

Section: Fusion Motivation Of Global Structure and Local Structurementioning

confidence: 99%

Global Plus Local Projection to Latent Structures

Jing

Zhou

Chen

2022

Intelligent Control and Learning Systems

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Owing to the raised demands on process operation and product quality, the modern industrial process becomes more complicated when accompanied by the large number of process and quality variables produced. Therefore, quality-related fault detection and diagnosis are extremely necessary for complex industrial processes. Data-driven statistical process monitoring plays an important role in this topic for digging out the useful information from these highly correlated process and quality variables, because the quality variables are measured at a much lower frequency and usually have a significant time delay (Ding 2014; Aumi et al. 2013; Peng et al. 2015; Zhang et al. 2016; Yin et al. 2014). Monitoring the process variables related to the quality variables is significant for finding potential harm that may lead to system shutdown with possible enormous economic loss.

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“…In comparison with CoJIT, LW-PLS can be suitable alternative approach as it can cope with outliers [6,11] as well as collinearity and nonlinearity among process variables [2]. On the other hand, linear PLS regression in LW-PLS may not function well when processes behave highly nonlinear characteristics [12][13][14]. Thus, an improved algorithm for LW-PLS based adaptive soft sensors which is capable of dealing with highly nonlinear data is required.…”

Section: Distillationmentioning

confidence: 99%

“…QPLS, SPLS and NNPLS which are internal nonlinear PLS (NPLS) models make use of nonlinear functions (e. g. simple polynomial transformation of observed data) to define relationship between latent variables (not directly observed variables) [15][16][17]. However, predefined form of quadratic function in QPLS algorithms has restricted its flexibility to develop nonlinear model [13,[18][19][20]. Although SPLS and NNPLS algorithms provide flexibility to capture nonlinearity relationships for variables, these methods may lead to local minima or obtain over-fitted model [18,19,21].…”

Section: Distillationmentioning

confidence: 99%

Development of Adaptive Soft Sensor Using Locally Weighted Kernel Partial Least Square Model

Yeo

Saptoro

Perumal

2017

Chemical Product and Process Modeling

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Locally weighted partial least square (LW-PLS) model has been commonly used to develop adaptive soft sensors and process monitoring for numerous industries which include pharmaceutical, petrochemical, semiconductor, wastewater treatment system and biochemical. The advantages of LW-PLS model are its ability to deal with a large number of input variables, collinearity among the variables and outliers. Nevertheless, since most industrial processes are highly nonlinear, a traditional LW-PLS which is based on a linear model becomes incapable of handling nonlinear processes. Hence, an improved LW-PLS model is required to enhance the adaptive soft sensors in dealing with data nonlinearity. In this work, Kernel function which has nonlinear features was incorporated into LW-PLS model and this proposed model is named locally weighted kernel partial least square (LW-KPLS). Comparisons between LW-PLS and LW-KPLS models in terms of predictive performance and their computational loads were carried out by evaluating both models using data generated from a simulated plant. From the results, it is apparent that in terms of predictive performance LW-KPLS is superior compared to LW-PLS. However, it is found that computational load of LW-KPLS is higher than LW-PLS. After adapting ensemble method with LW-KPLS, computational loads of both models were found to be comparable. These indicate that LW-KPLS performs better than LW-PLS in nonlinear process applications. In addition, evaluation on localization parameter in both LW-PLS and LW-KPLS is also carried out.

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A nonlinear partial least squares with slice transform based piecewise linear inner relation

Cited by 18 publications

References 30 publications

Artificial neural networks (ANNS) versus partial least squares (PLS) for spectral interference correction for taking part of the lab to the sample types of applications: an experimental study

Artificial neural networks (ANNS) versus partial least squares (PLS) for spectral interference correction for taking part of the lab to the sample types of applications: an experimental study

Global Plus Local Projection to Latent Structures

Development of Adaptive Soft Sensor Using Locally Weighted Kernel Partial Least Square Model

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