Search

For the regression problem of support vector machine, the solution processes of the most existing methods use offline datasets, which cannot be realized online. For this problem, this paper presents a new online approach to identify these unknown parameters contained in the support vector machine. A new cost function is constructed by substituting the error term into the standard cost function, which is different from the standard support vector machine, and the gradient descent approach is then used to minimize the newly created loss function, thus proposing a stochastic gradient support vector machine algorithm to estimate the unknown parameters based on the recursive identification methods. Furthermore, to advance the property of the stochastic gradient support vector machine algorithm, a moving data window is used to widen the scalar information into a fixed-length innovation vector, thereby increasing the amount of information used in the parameter estimation based on the multi-innovation identification theory. In addition, the forgetting factor is brought into the proposed algorithms, and the corresponding forgetting factor recursive algorithms are derived. These methods are recursive identification methods, which may be implemented online and are more efficient in terms of computing. Finally, utilizing the MatLab platform, the validity and usefulness of the explored methodologies are proven using several numerical simulation examples. • Present a stochastic gradient support vector machine algorithm by a recursive scheme. • Present a multi-innovation stochastic gradient support vector machine algorithm. • Use the forgetting factor to improve the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

To accurately describe the operating mechanism of supercapacitor (SC), a precise model should be established for the SC. In this paper, an equivalent-circuit model with a controlled current source is proposed, which can simulate the dynamic behavior of the SC. The recursive least square algorithm with forgetting factor is introduced to identify model parameters. Experimental and simulation results are presented to verify the proposed equivalent-circuit model. Results show a very good performance since the maximum relative modeling error do not exceed 1%. Therefore, the proposed model with its parameter identification method can effectively reveal the operating mechanism of the SC. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A dependable long-term prediction of rock displacement surrounding a tunnel is an effective way to predict rock displacement values in the future. A multi-step-ahead prediction model, which is based on a Support Vector Machine (SVM), is proposed for predicting rock displacement surrounding a tunnel. To improve the performance of SVM, parameter identification is used for SVM. In addition, to treat the time-varying features of rock displacement surrounding a tunnel, a forgetting factor is introduced to adjust the weights between new and old data. Finally, data from the Chijiangchong tunnel are selected to examine the performance of the prediction model. Comparative results presented between SVMFF (SVM with a forgetting factor) and an Artificial Neural Network with a Forgetting Factor (ANNFF) show that SVMFF is generally better than ANNFF. This indicates that a forgetting factor can effectively improve the performance of SVM, especially for time-varying problems. [ABSTRACT FROM AUTHOR]

The article discusses a study which examines the effects of a forgetting factor on the recursive least squares (RLS) dictionary learning.