Counterpropagation Fuzzy-Neural Network for Stream Flow Estimation
Resource
農業工程學報,44(2),26-38
Journal
農業工程學報
Journal Volume
44
Journal Issue
2
Pages
26-38
Date Issued
1998-06
Date
1998-06
Author(s)
Abstract
This research presents a fuzzy-neural approach to the prediction of nonlinear function and the estimation of stream flow. The fuzzy-neural network is constructed by a set of Rule-Base control, a modified self-organizing counter propagation network and a fuzzy control predictor on the basis of the extracted rules in its predicting part. The algorithm is investigated on the prediction of two nonlinear functions which are generated by the Monte Carlo method and it then is applied on the estimation of flow of the Ta-Chia river in Taiwan. The terms of the high prediction accuracy. Two strategies are proposed to enhance the algorithm, i.e. separated the training sample set and used Gaussian membership function. Substantial improvements in estimated river flow are obtained.
Subjects
Fuzzy-neural network
Rule-base control
Counterpropagation network
Fuzzy control
Estimation of flow
Type
journal article
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反傳遞模糊類神經網路於流量推估之應用.pdf
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