Modeling the effect of manufacturing precision on electrical performance of filters using support vector regression

This paper presents a data-driven modeling method for cavity filters. In the method, according to the data set from the manufacturing of filters, a model that reveals the effect of manufacturing precision on electrical performance of cavity filters is firstly developed by linear programming support vector regression. Moreover, to solve the modeling problem of small data set, multi-kernel and prior knowledge are utilized to modify the traditional linear programming support vector regression. Finally, some experiments are carried out, and the experimental results confirm the effectiveness of the modified arithmetic and the modeling method for cavity filter. The model is particularly suited to the computer-aided manufacturing and automatic tuning system of volume-producing filters.

[1]  Kawthar A. Zaki,et al.  Computer-aided diagnosis and tuning of cascaded coupled resonators filters , 2002 .

[2]  Jiaxin Wang,et al.  Non-flat function estimation with a multi-scale support vector regression , 2006, Neurocomputing.

[3]  C.F.N. Cowan,et al.  Intelligent alignment of waveguide filters using a machine learning approach , 1989 .

[4]  Gérard Bloch,et al.  Support vector regression from simulation data and few experimental samples , 2008, Inf. Sci..

[5]  Gunnar Rätsch,et al.  Large Scale Multiple Kernel Learning , 2006, J. Mach. Learn. Res..

[6]  H.C. Bell.  The Coupling Matrix in Low-Pass Prototype Filters , 2007, IEEE Microwave Magazine.

[7]  Jia-Sheng Hong,et al.  Microstrip filters for RF/microwave applications , 2001 .

[8]  V. Miraftab,et al.  Computer-aided tuning of microwave filters using fuzzy logic , 2002, 2002 IEEE MTT-S International Microwave Symposium Digest (Cat. No.02CH37278).

[9]  Yanfang Yu,et al.  Multi-scale linear programming support vector regression for ethylene distillation modeling. , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[10]  Raafat R. Mansour,et al.  Fully Automated RF/Microwave Filter Tuning by Extracting Human Experience Using Fuzzy Controllers , 2008, IEEE Transactions on Circuits and Systems I: Regular Papers.

[11]  M. Cacciola,et al.  Microwave Devices and Antennas Modelling by Support Vector Regression Machines , 2006, IEEE Transactions on Magnetics.

[12]  Christos Christodoulou,et al.  Support Vector Machines for Antenna Array Processing and Electromagnetics , 2006, Support Vector Machines for Antenna Array Processing and Electromagnetics.

[13]  Zhao Lu,et al.  Non-Mercer hybrid kernel for linear programming support vector regression in nonlinear systems identification , 2009, Appl. Soft Comput..

[14]  G. Macchiarella,et al.  Computer-aided tuning of GSM base-station filters - experimental results , 2006, 2006 IEEE Radio and Wireless Symposium.

[15]  R.V. Snyder,et al.  Practical Aspects of Microwave Filter Development , 2006, 2006 International Conference on Microwaves, Radar & Wireless Communications.

[16]  P. Harscher,et al.  Automated filter tuning using generalized low-pass prototype networks and gradient-based parameter extraction , 2001 .