Kernel classifier with adaptive structure and fixed memory for process diagnosis

Wang, Haiqing; Li, Ping; Gao, Furong; Song, Zhihuan; Ding, Steven X.

doi:10.1002/aic.10982

Cited by 31 publications

(59 citation statements)

References 37 publications

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“…The performance criteria calculation formulas were derived from Eqs. (28) to (30) where N denotes the number of data patterns in the data set; and y i andŷ i stand for the actual value and the estimated value of one data point i, respectively.…”

Section: Performance Criteriamentioning

confidence: 99%

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Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm

Zhang

Niu

et al. 2013

Knowledge-Based Systems

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Section: Performance Criteriamentioning

confidence: 99%

“…Sparse treatment, based on a pruning method, was conducted before training to reduce the computational burden in later work. An adaptive kernel learning network classifier was developed for process diagnosis in [30]. A two-step learning strategy was performed.…”

Section: Related Workmentioning

confidence: 99%

Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm

Zhang

Niu

et al. 2013

Knowledge-Based Systems

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“…By using this method, the sparsity decreasing strategy is implemented that overdue SVs are eliminated one by one in a different manner to Ref. [10].…”

Section: ) Least Supported Sv Eliminationmentioning

confidence: 99%

“…In our former studies [9,10,11,12], an adaptive kernel learning (AKL) algorithm was presented to control the model complexity with a sequentially sparse strategy. Meanwhile, it adopts a two-stage recursive learning mechanism to update the network topology effectively which could trace different characteristics of the process.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive least contribution elimination kernel learning approach for rubber mixing soft-sensing modeling

Gao

Wang

et al. 2010

2010 IEEE International Conference on Intelligent Computing and Intelligent Systems

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Rubber mixing process is a typical non-linear fed batch process without well developed mechanism. Soft-sensing modeling of the mixture's Mooney viscosity is crucial and challenging since this index is an important process criterion to judge the quality of rubber compounds while the measurement of Mooney viscosity is time-consuming and laborious to assay.Furthermore, the mixing process is drifting and volatile even noisy; only few data samples could be used to modeling. In present paper, an adaptive least contribution elimination kernel learning (ALCEKL) approach is proposed to predict the Mooney viscosity. It adopts a sparsity strategy of least contribution elimination and presents a buffer based learning algorithm associated with improved space angle index (SAl) strategy. Experiments on field data indicate that proposed approach is more robust and accurate than other kernelized modeling methods with feasible computational complexity under field circumstances.

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Spectroscopic models for real‐time monitoring of cell culture processes using spatiotemporal just‐in‐time Gaussian processes

et al. 2021

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Spectroscopic methods play an instrumental role in the implementation of the U.S. Food and Drug Administration outlined process analytical technology for biopharmaceutical manufacturing. Industrial spectroscopic calibration models are typically developed in an offline setting using traditional methods, such as partial least squares and principal component regression. Apart from the limiting performances of these conventional models under time‐varying operating conditions, these methods require access to extensive historical data, which are seldom available in biopharmaceutical manufacturing. In this article, we propose a novel spatiotemporal just‐in‐time learning (ST‐JITL) based spectroscopic model calibration platform for automatic training and maintenance of calibration models using routine batch data. The proposed ST‐JITL framework uses Gaussian processes (GPs) for local model calibration. A GP model not only exhibits superior performance over traditional methods but also provides credibility intervals around the model predictions. The efficacy of the ST‐JITL based model calibration platform is demonstrated in predicting the critical performance parameters of an industrial cell culture process.

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Kernel classifier with adaptive structure and fixed memory for process diagnosis

Cited by 31 publications

References 37 publications

Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm

Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm

Adaptive least contribution elimination kernel learning approach for rubber mixing soft-sensing modeling

Spectroscopic models for real‐time monitoring of cell culture processes using spatiotemporal just‐in‐time Gaussian processes

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