Crystal filter tuning using machine learning.
type: CreativeWork
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-a(busting 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 th…
abstract: 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-a(busting 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 th…
author: Tsaptsinos, Dimitris
language: en
date published: 1992-03
date modified: 2026-07-24T06:31:00Z
provider: Sheffield Hallam University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Sheffield City Polytechnic