Support vector machine based semi-active control of structures: a new control strategy

2009 ◽  
Vol 20 (6) ◽  
pp. 711-720 ◽  
Author(s):  
Chunxiang Li ◽  
Qing Liu
Keyword(s):  
Support Vector Machine ◽  
Active Control ◽  
Control Strategy ◽  
Support Vector

scholarly journals Least Squares Support Vector Machine-Based Multivariate Generalized Predictive Control for Parabolic Distributed Parameter Systems with Control Constraints

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 453
Author(s):  
Ling Ai ◽  
Yang Xu ◽  
Liwei Deng ◽  
Kok Lay Teo
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Predictive Control ◽  
Control Strategy ◽  
Distributed Parameter Systems ◽  
Diffusion Reaction ◽  
Support Vector ◽  
Distributed Parameter ◽  
Generalized Predictive Control ◽  
Parabolic Distributed Parameter Systems

This manuscript addresses a new multivariate generalized predictive control strategy using the least squares support vector machine for parabolic distributed parameter systems. First, a set of proper orthogonal decomposition-based spatial basis functions constructed from a carefully selected set of data is used in a Galerkin projection for the building of an approximate low-dimensional lumped parameter systems. Then, the temporal autoregressive exogenous model obtained by the least squares support vector machine is applied in the design of a multivariate generalized predictive control strategy. Finally, the effectiveness of the proposed multivariate generalized predictive control strategy is verified through a numerical simulation study on a typical diffusion-reaction process in radical symmetry.

scholarly journals Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 426 ◽  
Author(s):  
Yongliang Zheng ◽  
Feng He ◽  
Xinze Shen ◽  
Xuesheng Jiang
Keyword(s):  
Support Vector Machine ◽  
Fuel Cell ◽  
Fuzzy Control ◽  
Working Conditions ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Control Strategies ◽  
Least Square ◽  
Support Vector ◽  
Fuzzy Control Strategy

Aimed at the limitation of traditional fuzzy control strategy in distributing power and improving the economy of a fuel cell hybrid electric vehicle (FCHEV), an energy management strategy combined with working conditions identification is proposed. Feature parameters extraction and sample divisions were carried out for typical working conditions, and working conditions were identified by the least square support vector machine (LSSVM) optimized by grid search and cross validation (CV). The corresponding fuzzy control strategies were formulated under different typical working conditions, in addition, the fuzzy control strategy was optimized with total equivalent energy consumption as the goal by particle swarm optimization (PSO). The adaptive switching of fuzzy control strategies under different working conditions were realized through the identification of driving conditions. Results showed that the fuzzy control strategy with the function of driving conditions identification had a more efficient power distribution and better economy.

scholarly journals Application of Support Vector Machine-Based Semiactive Control for Seismic Protection of Structures with Magnetorheological Dampers

2012 ◽  
Vol 2012 ◽  
pp. 1-18
Author(s):  
Chunxiang Li ◽  
Qing Liu ◽  
Shengning Lan
Keyword(s):  
Support Vector Machine ◽  
Control Strategy ◽  
Control Strategies ◽  
Seismic Protection ◽  
Support Vector ◽  
Semiactive Control ◽  
Mr Dampers ◽  
Acceleration Amplification ◽  
Switch Control ◽  
Lqr Controller

Based on recent research by Li and Liu in 2011, this paper proposes the application of support vector machine- (SVM-) based semiactive control methodology for seismic protection of structures with magnetorheological (MR) dampers. An important and challenging task of designing the MR dampers is to develop an effective semiactive control strategy that can fully exploit the capabilities of MR dampers. However, amplification of the local acceleration response of structures exists in the widely used semiactive control strategies, namely “Switch” control strategies. Then the SVM-based semiactive control strategy has been employed to design MR dampers. Firstly, the LQR controller for the numerical model of a multistory structure formulated using the dynamic dense method is constructed by using the classic LQR control theory. Secondly, an SVM model which comprises the observers and controllers in the control system is designed and trained to emulate the performance of the LQR controller. Finally, an online autofeedback semiactive control strategy is developed by resorting to SVM and then used for designing MR dampers. Simulation results show that the MR dampers utilizing the SVM-based semiactive control algorithm, which eliminates the local acceleration amplification phenomenon, can remarkably reduce the displacement, velocity, and acceleration responses of the structure.

Compressor Surge Detection Based on Support Vector Data Description

2012 ◽  
Vol 152-154 ◽  
pp. 1545-1549
Author(s):  
Chang Zheng Li ◽  
Yong Lei
Keyword(s):  
Support Vector Machine ◽  
Active Control ◽  
Axial Flow ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Compressor Surge ◽  
Aerodynamic Instability ◽  
One Class Classification

Axial flow compressors work as an indispensable device in industry fields. Surge is a phenomenon of aerodynamic instability, which characterized by disruption of flow. When a compressor works in surge state, the vibration is so intense that it may causes accidents. Detecting surge timely and accurately not only insure safety of compressors but also is a key of active control of aerodynamic instability. Support vector data description (SVDD) is a one-class classification method developed based on the theory of support vector machine (SVM). In this paper, we introduce SVDD into the field of compressor surge detection. It demonstrates that SVDD method can give a warning far ahead of surge.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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