中国科学院深圳先进技术研究院机构知识库(SIAT OpenIR): Real-time Tuning of Cavity Filters by Learning from Human Experience: A Vector Field Approach
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Real-time Tuning of Cavity Filters by Learning from Human Experience: A Vector Field Approach
Zhiyang Wang; Shaokun Jin; Jingfeng Yang; Xinyu Wu; Yongsheng Ou
2016
Conference NameWorld Congress on Intelligent Control and Automation(WCICA)
Conference Place中国桂林
AbstractThe technique of tuning a cavity filter is purely a rule of thumb: only experienced tuning engineer is competent to the task. However, with the great development of the communication industry and the rapid increasing of production capacity, the need for tuning technicians becomes urgent. It is meaningful to replace this traditional manual tuning task with some more advanced and automatic methods. We hereby propose a real-time computer-aided tuning method based on the vector field approximating approach, which can be applied in robotic tuning systems in the near future. In this paper, we first make a literature review on some previous intelligent cavity filter tuning solutions. Then the method of employing vector fields to represent the change of S-parameters is proposed. We provide concrete procedures to drive the S-parameters curves to approximate towards the target. In the end, we give the experimental results which validate the flexibility of the method
Department智能仿生中心
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.siat.ac.cn/handle/172644/10131
Collection集成所
Affiliation2016
Recommended Citation
GB/T 7714
Zhiyang Wang,Shaokun Jin,Jingfeng Yang,et al. Real-time Tuning of Cavity Filters by Learning from Human Experience: A Vector Field Approach[C],2016.
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