Automatic fine tuning of cavity filters
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Automatisk finjustering av kavitetsfilter (Swedish)
Cavity filters are a necessary component in base stations used for telecommunication. Without these filters it would not be possible for base stations to send and receive signals at the same time. Today these cavity filters require fine tuning by humans before they can be deployed. This thesis have designed and implemented a neural network that can tune cavity filters. Different types of design parameters have been evaluated, such as neural network architecture, data presentation and data preprocessing. While the results was not comparable to human fine tuning, it was shown that there was a relationship between error and number of weights in the neural network. The thesis also presents some rules of thumb for future designs of neural network used for filter tuning.
Place, publisher, year, edition, pages
2016. , 49 p.
Artificiell intelligens, Neural network, filter, fine tuning, AI, ANN
IdentifiersURN: urn:nbn:se:liu:diva-129576ISRN: LIU-IDA/LITH-EX-A--16/036--SEOAI: oai:DiVA.org:liu-129576DiVA: diva2:941189
Subject / course
2016-06-10, Alan Turing, 09:00 (English)