caa a22 4500 605469091 CHVBK 20210128100320.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s00500-014-1390-x doi (NATIONALLICENCE)springer-10.1007/s00500-014-1390-x Incorporating prior knowledge and multi-kernel into linear programming support vector regression [Elektronische Daten] [Jinzhu Zhou, Baoyan Duan, Jin Huang, Na Li] This paper proposes a multi-kernel linear program support vector regression with prior knowledge in order to obtain an accurate data-driven model in the case of an insufficient amount of measured data. In the algorithm, multiple feature spaces have been utilized to incorporate multi-kernel functions into the framework of linear programming support vector regression (LPSVR), and then the prior knowledge which may be exact or biased from a calibrated physical simulator has also been incorporated into LPSVR by modifying optimization formulations. Moreover, a strategy of parameter selections for the proposed algorithm has been presented to facilitate practical applications. Some experiments from a synthetic example, a microstrip antenna and six-pole microwave filter have been carried out, and the experimental results show that the proposed algorithm can obtain a satisfactory data-based model in the case of the scarcity of measured data. The proposed algorithm shows great potentialities in some applications where the experimental data are insufficient for an accurate data-driven model and the prior knowledge from a calibrated physical simulator of practical applications is available. Springer-Verlag Berlin Heidelberg, 2014 Linear programming support vector regression nationallicence Prior knowledge nationallicence Multi-kernel nationallicence Insufficient data nationallicence Data-driven model nationallicence Zhou Jinzhu Key Laboratory of Electronic Equipment Structure Design of Ministry of Education, Xidian University, Xi'an, Shaanxi, People's Republic of China aut Duan Baoyan Key Laboratory of Electronic Equipment Structure Design of Ministry of Education, Xidian University, Xi'an, Shaanxi, People's Republic of China aut Huang Jin Key Laboratory of Electronic Equipment Structure Design of Ministry of Education, Xidian University, Xi'an, Shaanxi, People's Republic of China aut Li Na Key Laboratory of Electronic Equipment Structure Design of Ministry of Education, Xidian University, Xi'an, Shaanxi, People's Republic of China aut Soft Computing Springer Berlin Heidelberg 19/7(2015-07-01), 2047-2061 1432-7643 19:7<2047 2015 19 500 https://doi.org/10.1007/s00500-014-1390-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1390-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhou Jinzhu Key Laboratory of Electronic Equipment Structure Design of Ministry of Education, Xidian University, Xi'an, Shaanxi, People's Republic of China aut NATIONALLICENCE 700 1- Duan Baoyan Key Laboratory of Electronic Equipment Structure Design of Ministry of Education, Xidian University, Xi'an, Shaanxi, People's Republic of China aut NATIONALLICENCE 700 1- Huang Jin Key Laboratory of Electronic Equipment Structure Design of Ministry of Education, Xidian University, Xi'an, Shaanxi, People's Republic of China aut NATIONALLICENCE 700 1- Li Na Key Laboratory of Electronic Equipment Structure Design of Ministry of Education, Xidian University, Xi'an, Shaanxi, People's Republic of China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/7(2015-07-01), 2047-2061 1432-7643 19:7<2047 2015 19 500