• Title/Summary/Keyword: functional link associative neural network

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Monitoring of Chemical Processes Using Modified Scale Space Filtering and Functional-Link-Associative Neural Network (개선된 스케일 스페이스 필터링과 함수연결연상 신경망을 이용한 화학공정 감시)

  • Park, Jung-Hwan;Kim, Yoon-Sik;Chang, Tae-Suk;Yoon, En-Sup
    • Journal of Institute of Control, Robotics and Systems
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    • v.6 no.12
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    • pp.1113-1119
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    • 2000
  • To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

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The Analysis of a Process Monitoring system based on Functional Link Associative Network (화학공정 감시를 위한 함수연결연상 신경망 시스템 구현)

  • Yoon En Sup;Cho Jae Kyu;Lee Dong Eon;Kim Yong Ha;Ahn Sung Jun
    • Journal of the Korean Institute of Gas
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    • v.7 no.3 s.20
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    • pp.24-31
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    • 2003
  • To operate process plant safely and economically, process monitoring is very important. There are a great number of data acquired through distributed control system and process information system. Fault monitoring is the task with difficulties owing to not only the huge amount of data, but also nonlinearity of chemical processes. In this research, the program, REFA, based on PCA and functional link associative neural network has developed. REFA has better learning capabilities, generalization abilities, and shorter learning time than existing neural network programs. In this work its usefulness has proven by application to Tennessee Eastman process.

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