Multivariate data modeling using modified kernel partial least squares

Gao, Yingbin; Kong, Xiangyu; Hu, Changhua; Zhang, Zhengxin; Li, Hongzeng; Hou, Lijun

doi:10.1016/j.cherd.2014.09.004

Cited by 15 publications

(13 citation statements)

References 34 publications

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“…A model is considered acceptable when it has the R 2 value, which is more than 0.7. 63,64 Besides RMSE, mean absolute percentage error for training data (MAPE 1 ) and test data (MAPE 2 ) were also employed to present model performance. The expression of mean absolute percentage error MAPE is described below: 65…”

Section: Performance Measurement Methods For Prediction Modelsmentioning

confidence: 99%

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Missing data treatment for locally weighted partial least square‐based modelling: A comparative study

Yeo

Saptoro

Perumal

2020

Asia-Pacific J Chem Eng

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Adaptive soft sensors including widely used locally weighted partial least square (LW‐PLS) have been established for online prediction, fault detection, and process monitoring. Nevertheless, majority of these existing adaptive soft sensors have zero tolerance to missing data, and the presence of missing data is inevitable due to sensor failures, routine maintenance, changes in sensor equipment over time, merging data from different system, and so forth. In the literature, limited studies could be found on the effects of missing data and the existing missing data imputation methods on the predictive performances of adaptive soft sensors. This work reports combined use of different missing data imputation techniques on LW‐PLS and the nonadaptive soft sensor, the partial least square (PLS). Well known trimmed score regression (TSR) and singular value decomposition (SVD) were employed in this study, and thus, the newly integrated TSR‐LW‐PLS and SVD‐LW‐PLS algorithms were proposed. Meanwhile, both existing TSR‐PLS and SVD‐PLS were used, and their results compared with the novel TSR‐LW‐PLS and SVD‐LW‐PLS algorithms. These algorithms were tested and evaluated using two examples having different percentages of missing data ranging from 5% to 40%. Results showed that TSR‐LW‐PLS model was superior compared with SVD‐LW‐PLS, TSR‐PLS, and SVD‐PLS as indicated by more than 100% improvements in prediction accuracy. It was also evident that TSR‐LW‐PLS is able to cope well with up to 20% of missing data.

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Section: Performance Measurement Methods For Prediction Modelsmentioning

confidence: 99%

“…The closer the R 2 value to 1, the better the correlation between the estimated and the actual values will be. A model is considered acceptable when it has the R 2 value, which is more than 0.7 …”

Section: Preliminariesmentioning

confidence: 99%

Missing data treatment for locally weighted partial least square‐based modelling: A comparative study

Yeo

Saptoro

Perumal

2020

Asia-Pacific J Chem Eng

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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%

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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“…Meanwhile, over-fitting issue remains a controversial issue in the academic field. In order to deal with the problem, many approaches were proposed to eliminate the over-fitting phenomena [3].…”

Section: Introductionmentioning

confidence: 99%

Combined General Vector Machine for Single Point Electricity Load Forecast

Yong

Wei

Shen

et al. 2020

Lecture Notes in Electrical Engineering

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General Vector Machine (GVM) is a newly proposed machine learning model, which is applicable to small samples forecast scenarios. In this paper, the GYM is applied into electricity load forecast based on single point modeling method. Meanwhile, traditional time series forecast models, including back propagation neural network (BPNN), Support Vector Machine (SVM) and Autoregressive Integrated Moving Average Model (ARIMA), are also experimented for single point electricity load forecast. Further, the combined model based on GYM, BPNN, SVM and ARIMA are proposed and verified. Results show that GYM performs better than these traditional models, and the combined model outperforms any other single models for single point electricity load forecast.

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Multivariate data modeling using modified kernel partial least squares

Cited by 15 publications

References 34 publications

Missing data treatment for locally weighted partial least square‐based modelling: A comparative study

Missing data treatment for locally weighted partial least square‐based modelling: A comparative study

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

Combined General Vector Machine for Single Point Electricity Load Forecast

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