Crystal filter tuning using machine learning

Manual tuning of electronic filters represents a time-consuming process which can benefit from some computer assistance. A prototype computer-based system for the tuning of crystal filters after manufacture was developed. This system solved the problem of crystal filter tuning in a novel way. The sy...

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Main Author: Tsaptsinos, Dimitris
Published: Sheffield Hallam University 1992
Subjects:
Online Access:http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306227
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spelling ndltd-bl.uk-oai-ethos.bl.uk-3062272015-03-19T05:10:15ZCrystal filter tuning using machine learningTsaptsinos, Dimitris1992Manual tuning of electronic filters represents a time-consuming process which can benefit from some computer assistance. A prototype computer-based system for the tuning of crystal filters after manufacture was developed. This system solved the problem of crystal filter tuning in a novel way. The system, called AEK (Applied Expert Knowledge), was developed using crystal filters and is a hybrid system with the following two functions: (1) Required values of features are extracted from the filter waveform and passed to the expert system which determines the component to adjust and the direction to turn, or the end of the tuning. (2) Sampled values of the waveform are extracted and passed to a neural network which determines how far to turn the component chosen in (1). The prominent aspects were: - Work using the protocol analysis elicitation technique indicated the need to separate the process into two sub-tasks (stopband and passband). Each sub-task was divided into three classification parts which determined (i) the continuation of the tuning process, (ii) the component and direction to turn, and (iii) the distance to turn respectively. Unfortunately, it was not possible to extract rules from the operator. - Three learning techniques (IID3, Adaptive Combiners, Neural Networks) were used and compared as the means of automated knowledge elicitation. All three techniques used case knowledge in the form of examples. The investigations suggested the use of ID3 for the first two parts of each subtask employing features with linguistic values. The number of linguistic values each feature has, was also derived. - Neural networks were trained for the third part. It was necessary to have one network for each component/direction combination and to use examples from just one mal-adjusting process. - Tests of the hybrid system for a number of cases indicated that it performed as well as a skilled operator, and that it can be used by novice operators but situations arose where there was either no knowledge or contradictory knowledge. The prototype system was developed using one type of crystal filters but the generic construction procedure can be followed to build other systems for other types.670.285Computer Aided ManufacturingSheffield Hallam Universityhttp://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306227http://shura.shu.ac.uk/3145/Electronic Thesis or Dissertation
collection NDLTD
sources NDLTD
topic 670.285
Computer Aided Manufacturing
spellingShingle 670.285
Computer Aided Manufacturing
Tsaptsinos, Dimitris
Crystal filter tuning using machine learning
description Manual tuning of electronic filters represents a time-consuming process which can benefit from some computer assistance. A prototype computer-based system for the tuning of crystal filters after manufacture was developed. This system solved the problem of crystal filter tuning in a novel way. The system, called AEK (Applied Expert Knowledge), was developed using crystal filters and is a hybrid system with the following two functions: (1) Required values of features are extracted from the filter waveform and passed to the expert system which determines the component to adjust and the direction to turn, or the end of the tuning. (2) Sampled values of the waveform are extracted and passed to a neural network which determines how far to turn the component chosen in (1). The prominent aspects were: - Work using the protocol analysis elicitation technique indicated the need to separate the process into two sub-tasks (stopband and passband). Each sub-task was divided into three classification parts which determined (i) the continuation of the tuning process, (ii) the component and direction to turn, and (iii) the distance to turn respectively. Unfortunately, it was not possible to extract rules from the operator. - Three learning techniques (IID3, Adaptive Combiners, Neural Networks) were used and compared as the means of automated knowledge elicitation. All three techniques used case knowledge in the form of examples. The investigations suggested the use of ID3 for the first two parts of each subtask employing features with linguistic values. The number of linguistic values each feature has, was also derived. - Neural networks were trained for the third part. It was necessary to have one network for each component/direction combination and to use examples from just one mal-adjusting process. - Tests of the hybrid system for a number of cases indicated that it performed as well as a skilled operator, and that it can be used by novice operators but situations arose where there was either no knowledge or contradictory knowledge. The prototype system was developed using one type of crystal filters but the generic construction procedure can be followed to build other systems for other types.
author Tsaptsinos, Dimitris
author_facet Tsaptsinos, Dimitris
author_sort Tsaptsinos, Dimitris
title Crystal filter tuning using machine learning
title_short Crystal filter tuning using machine learning
title_full Crystal filter tuning using machine learning
title_fullStr Crystal filter tuning using machine learning
title_full_unstemmed Crystal filter tuning using machine learning
title_sort crystal filter tuning using machine learning
publisher Sheffield Hallam University
publishDate 1992
url http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306227
work_keys_str_mv AT tsaptsinosdimitris crystalfiltertuningusingmachinelearning
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