Abstract
This paper presents a new method of sequential microwave fllter tuning. For fllters with R tuning elements (including cavities, couplings and cross-couplings), based on physically measured scattering characteristics in the frequency domain, the Artiflcial Neural Network (ANN) is used to build inverse models of R sub-fllters. Each sub-fllter is associated to one tuning element. The sub-fllters are obtained by successive opening or shorting of resonators and by removing coupling screws. For each sub-fllter, the ANN training vectors are deflned as physical re∞ection characteristics (input vectors) and the corresponding positions of the tuning element, which is detuned, in both directions, from its proper setting (output vectors). In the tuning process, such inverse models are used for calculating the tuning element increments needed for setting the tuning element in the proper position. The tuning experiment, conducted on 8- and 11-cavity fllters, has shown the performance of the presented method.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
