Study on evolution methods for the optimization of machine learning models based on FT-NIR spectroscopy

Mo, Lina; Chen, Huazhou; Chen, Wei-Hao; Feng, Qinghua; Xu, Lili

doi:10.1016/j.infrared.2020.103366

Cited by 16 publications

(4 citation statements)

References 38 publications

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“…This process is reaping by the GA until a high accuracy of prediction is achieved. Hence, the population's final output individual is the best parameter group [81,82]. Fig.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields

Abad

Ghorbani

Mohamadian

et al. 2022

Fuel

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“…This process is reaping by the GA until a high accuracy of prediction is achieved. Hence, the population's final output individual is the best parameter group [81,82]. Fig.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields

Abad

Ghorbani

Mohamadian

et al. 2022

Fuel

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“…The two main parameters of the RBF are the regularization parameter (γ) and the width parameter (σ 2 ). Different values of these two parameters lead to changes in the stability and predictive performance of the model [35]. Therefore, there is an urgent need to find optimization methods to optimize γ and σ 2 to improve LS-SVR's learning ability and generalization.…”

Section: Modeling Methodsmentioning

confidence: 99%

Optimization of Online Soluble Solids Content Detection Models for Apple Whole Fruit with Different Mode Spectra Combined with Spectral Correction and Model Fusion

Li,

Peng,

et al. 2024

Foods

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Soluble solids content (SSC) is one of the main quality indicators of apples, and it is important to improve the precision of online SSC detection of whole apple fruit. Therefore, the spectral pre-processing method of spectral-to-spectral ratio (S/S), as well as multiple characteristic wavelength member model fusion (MCMF) and characteristic wavelength and non-characteristic wavelength member model fusion (CNCMF) methods, were proposed for improving the detection performance of apple whole fruit SSC by diffuse reflection (DR), diffuse transmission (DT) and full transmission (FT) spectra. The modeling analysis showed that the S/S- partial least squares regression models for all three mode spectra had high prediction performance. After competitive adaptive reweighted sampling characteristic wavelength screening, the prediction performance of all three model spectra was improved. The particle swarm optimization–extreme learning machine models of MCMF and CNCMF had the most significant enhancement effect and could make all three mode spectra have high prediction performance. DR, DT, and FT spectra all had some prediction ability for apple whole fruit SSC, with FT spectra having the strongest prediction ability, followed by DT spectra. This study is of great significance and value for improving the accuracy of the online detection model of apple whole fruit SSC.

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“…The RBF-LSSVM models were established for the determination of wastewater MIS content. By the former machine learning experiences [35], it is worth to fuse the regularization parameter (γ) and the RBF kernel width (σ 2 ) together, for automatic tuning to search for the optimal combination of parameters. Deep training of ðγ, σÞ is necessary for observing a minimum predictive RMSEV in the calibration-validation process.…”

Section: Determination Of Mis Using the Parametric Rbf-lssvmmentioning

confidence: 99%

Machine Learning Framework for Intelligent Detection of Wastewater Pollution by IoT-Based Spectral Technology

Cai

Chen

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

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Industrial wastewater contains excessive micro insoluble solids (MIS) that probably cause environmental pollutions. Near-infrared (NIR) spectroscopy is an advanced technology for rapid detection of the complex targets in wastewater. An Internet of Things (IoT) platform would support intelligent application of the NIR technologies. The studies of intelligent chemometric methods mainly contribute to improve the NIR calibration model based on the IoT platform. With the development of artificial intelligence, the backward interval and synergy interval techniques were proposed in combination use with the least square support vector machine (LSSVM) method, for adaptive selection of the informative spectral wavelength variables. The radial basis function (RBF) kernel is applied for nonlinear mapping. The regulation parameter and the kernel width are fused together for smart optimization. In the design for waveband autofittings, the total of digital wavelengths in the full scanning range was split into 43 equivalent subintervals, and then, the back interval LSSVM (biLSSVM) and the synergy interval LSSVM (siLSSVM) models were both established for the improvement of prediction results based on the adaptive selection of quasidiscrete variable combination. In comparison with some common linear and nonlinear models, the best training model was acquired with the siLSSVM method while the best testing model was obtained with biLSSVM. The intelligent optimization of model parameters indicated that the proposed biLSSVM and siLSSVM deep learning methodologies are feasible to improve the model prediction results in rapid determination of the wastewater MIS content by the IoT-based NIR technology. The machine learning framework is prospectively applied to the fast assessment of the environmental risk of industrial pollutions and water safety.

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Study on evolution methods for the optimization of machine learning models based on FT-NIR spectroscopy

Cited by 16 publications

References 38 publications

Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields

Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields

Optimization of Online Soluble Solids Content Detection Models for Apple Whole Fruit with Different Mode Spectra Combined with Spectral Correction and Model Fusion

Machine Learning Framework for Intelligent Detection of Wastewater Pollution by IoT-Based Spectral Technology

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