Non-linear projection to latent structures revisited: the quadratic PLS algorithm

Baffi, G.; Martin, E.B.; Morris, A.J.

doi:10.1016/s0098-1354(98)00283-x

Cited by 169 publications

(107 citation statements)

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“…PLS provides a bilinear decomposition of the secondary variables X and primary variable Y matrices into a number of rank-one matrices in a similar mode to that of Principal Component Analysis (PCA) for a single data matrix. The kernel of the PLS procedure is the Non-linear Iterative Partial Least-Squares (NIPALS) arithmetic (Baffi et al, 1999;Park et al, 2010). This arithmetic unceasingly extracts each pair of corresponding latent variables as a linear combination of the secondary and primary variables (Equation 15 and …”

Section: Select Of Input Variables Based On Plsmentioning

confidence: 99%

“…It generally makes use of available process measurement data or prior knowledge on process mechanism to build predictive model for estimating the primary variables that cannot be easily measured by hardware based sensor in a real-time fashion (Kaneko and Funatsu, 2014;Chen et al, 2014;Shokri et al, 2015;Liu et al, 2015). Many soft sensor methods have been presented, including Partial Least Squares (PLS), Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Gaussian Process Regression (GPR) (Baffi et al, 1999;Wang et al, 2014;Acuña et al, 2014;Chen et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Soft Sensor Modeling of Product Concentration in Glutamate Fermentation using Gaussian Process Regression

Zheng¹,

Pan²

2016

American Journal of Biochemistry and Biotechnology

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Abstract:The on-line control of glutamate fermentation process is difficult, owing to the typical uncertainties of biochemical process and the lack of suitable on-line sensors for primary process variables. A prediction model based on Gaussian Process Regression (GPR) is presented to predict glutamate concentration online. First, Partial Least Squares (PLS) is applied to extract the features of the input secondary variables to reduce the number of the variables dimension and eliminate the correlation, through variables selection to reduce model complexity and improve model tracking performance. Validation was carried out in a 5 L fermentation tank for 10 batches glutamate fermentation process. Simulation results show that the proposed model outperforms the PLS and Support Vector Machine (SVM) model and the Root Mean Square Error (RMSE) are 1.59, 7.98 and 1.95, respectively. It can provide effective operation guidance for control and optimization of the glutamate fermentation process.

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Section: Select Of Input Variables Based On Plsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Soft Sensor Modeling of Product Concentration in Glutamate Fermentation using Gaussian Process Regression

Zheng¹,

Pan²

2016

American Journal of Biochemistry and Biotechnology

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“…Recognition of the nonlinearities can be achieved using intuitive methods, for example, which apply nonlinear transformations to the original variables or create an array of linear models spanning the whole operating range. More advanced methods have also been proposed including nonlinear extensions to PCA (Li et al 2000)， introducing nonlinear modifications to the relationship between the X and Y blocks in PLS (Baffi et al, 1999) or applying neural network, fuzzy logic, etc. methods to represent the nonlinear directly.…”

Section: Nonlinear Modeling Approachmentioning

confidence: 99%

Application of Statistical Methods for Gas Turbine Plant Operation Monitoring

Pan¹

2011

Advances in Gas Turbine Technology

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“…PLS is a very robust technique to interpolate correlated and noise data BAFFI et al (1999). In this section, the latent variables are defined as linear functions of input variables.…”

Section: Partial Least Squares (Pls) Inferential Modelmentioning

confidence: 99%

Quantifying Closed Loop Performance Based on on-Line Performance Indices

Farenzena

Trierweiler

2006

IFAC Proceedings Volumes

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This article aims to construct an "inference model" (IM ) that assesses the closed loop performance and robustness for SISO controllers, with no need of intrusive tests (i.e. set-point changes or open-loop tests). The input variables are indexes that can be easily calculated on-line (minimal variance, perceptual error, standard deviation, among others). The IM is generated for a large set of plants and tuning parameters. The possible inputs for the IM are 9 standard assessment measurements (e.g., Harris Index, standard deviation, etc) on-line available, commonly present in commercial tools . The target for IM is the closed loop open loop rise time ratio (Rt R ), Gain Margin (GM), and normalized (ISE). These values are obtained by intrusive tests. Four different classes of inferential models (i.e., Neural Networks, Neuro Fuzzy, PLS, and QPLS) are compared. The best results are obtained by Neural Network IM. The results obtained show that the methodology is very promising.

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Non-linear projection to latent structures revisited: the quadratic PLS algorithm

Cited by 169 publications

References 0 publications

Soft Sensor Modeling of Product Concentration in Glutamate Fermentation using Gaussian Process Regression

Soft Sensor Modeling of Product Concentration in Glutamate Fermentation using Gaussian Process Regression

Application of Statistical Methods for Gas Turbine Plant Operation Monitoring

Quantifying Closed Loop Performance Based on on-Line Performance Indices

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