Online prediction model based on support vector machine

Wang, Wenjian; Men, Changqian; Lu, Wen-Hsiang

doi:10.1016/j.neucom.2007.07.020

Cited by 162 publications

(73 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The main step of online support vector machine algorithm is, when adding a sample ( , )0 T to training set, we first judge whether the change ∆ is positive or negative [3,4]. That is:…”

Section: The Calculation Process Of Online Support Vector Machinementioning

confidence: 99%

Modeling of Turbine-Boiler Coordinated System Based on Online Support Vector Machine

Li¹,

Liu²

2018

Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

View full text Add to dashboard Cite

Abstract. The simplified non-linear unit model not only can provide the inherent rules by control system design, but also can be use as a simulation process to evaluate the performance of the control algorithms. Using the nonlinear mapping characteristics of Online Support Vector Machine (Online SVM), the dynamic mathematical models of turbine-boiler coordinated system were established, and the simulation research were conducted. Operation data simulation shows that the model can perfectly reflect the dynamic characteristics of turbine-boiler coordinated system. When a new condition appeared in the process which did not include in the working condition data, the model can take advantage of the new data to realize online update, adding a new support vector, thus has a good reappearance.

show abstract

“…The main step of online support vector machine algorithm is, when adding a sample ( , )0 T to training set, we first judge whether the change ∆ is positive or negative [3,4]. That is:…”

Section: The Calculation Process Of Online Support Vector Machinementioning

confidence: 99%

Modeling of Turbine-Boiler Coordinated System Based on Online Support Vector Machine

Li¹,

Liu²

2018

Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

View full text Add to dashboard Cite

show abstract

“…Predicting the profit function will benefit process monitoring and optimal scheduling. Here, the focus is on the data-driven prediction of the profit function.In recent years, computational intelligence techniques such as artificial neural networks (ANNs) [9][10][11][12][13][14] and support vector machine (SVM) [14][15][16][17][18][19] received more and more attention because of their ability to model nonlinear systems. SVM, developed by Vapnik, is based on the statistical theory [20,21].…”

mentioning

confidence: 99%

Prediction of the Profit Function for Industrial 2‐Keto‐L‐Gulonic Acid Cultivation

Cui

Jia³

et al. 2011

Chem Eng & Technol

View full text Add to dashboard Cite

The profit function is the generic criterion to describe the cost effect of a batch process. To focus on the prediction of the profit function for 2-keto-L-gulonic acid (2-KGA) cultivation, which is potentially applicable for process monitoring and optimal scheduling, rolling learning-prediction (RLP) based on a support vector machine (SVM) is applied. The RLP implies that the SVM training database is rolling updated as the batch of current interest proceeds, and the SVM learning is then repeated for the prediction. The database is further updated after termination of a batch. The updating procedures are investigated in detail. Pseudo-online prediction is carried out using the data from industrial-scale 2-KGA cultivation under actual and hypothetical inoculation sequences. The results indicate that the average relative prediction error is less than 5 % in the later phase of fermentation in all inoculation sequences. Introduction2-Keto-L-gulonic acid (2-KGA) is the precursor for the synthesis of L-ascorbic acid. It is typically produced by a mixed culture of Bacillus megaterium (B. megaterium) and Gluconobacter oxydans (G. oxydans) with L-sorbose as substrate [1][2][3]. In 2-KGA fermentation workshops, batch-to-batch variation in process performance is often observed, even though the same culture conditions are applied. This may result from many factors such as fluctuations in the seed quality and raw materials composition. For control engineers, batch-dependent variation between batches implies the potential to improve the process performance by dynamic optimization or optimal scheduling [4][5][6][7][8]. The profit function, which is defined as the gross profit of a batch over the production time, reflects the cost effect of the batch process and can be used as the index to evaluate the batch performance. Predicting the profit function will benefit process monitoring and optimal scheduling. Here, the focus is on the data-driven prediction of the profit function.In recent years, computational intelligence techniques such as artificial neural networks (ANNs) [9][10][11][12][13][14] and support vector machine (SVM) [14][15][16][17][18][19] received more and more attention because of their ability to model nonlinear systems. SVM, developed by Vapnik, is based on the statistical theory [20,21]. It implements the structural risk minimization (SRM) principle which is an approach to minimize the upper bound risk related to the generalization performance. The solid theoretical basis makes it advantageous in generalization and convergence [19][20][21]. In this work, rolling learning-prediction (RLP) based on an SVM is practiced to predict the profit function of industrial-scale 2-KGA cultivation. The RLP mechanism is adopted to deal with the time-variant property of fermentation, and it makes full use of the available data of the predicted batch by adding these real-time data into the training database of SVM to start a new round of SVM learning. When the prediction approach is online operated, new batches will arrive in ...

show abstract

“…Support Vector Machines (SVMs) are another type of statistical learning-articial neural network technique, based on the computational learning theory, which face the problem of minimization of the structural risk (Vapnik, 1995). An online method based on an SVM model was introduced in (Wang et al, 2008) to predict air pollutant levels in a time series of monitored air pollutant in Hong Kong downtown area. Even if we refer to MLP and SVM approaches as black-box methods, in as much as they are not based on an explicit model, they have generalization capabilities that make possible their application to not-stationary situations.…”

mentioning

confidence: 99%