文章基本信息

标题：Flow Regime Classification Using Artificial Neural Network Trained on Electrical Capacitance Tomography Sensor Data
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作者：Khursiah Zainal-Mokhtar ; Junita Mohamad-Saleh ; Hafizah Talib 等
期刊名称：Computer and Information Science
印刷版ISSN：1913-8989
电子版ISSN：1913-8997
出版年度：2008
卷号：1
期号：1
页码：25
DOI：10.5539/cis.v1n1P25
出版社：Canadian Center of Science and Education
摘要：The main goal of the presented work is to analyse the performance of the Multi-Layer Perceptron (MLP) neural network for flow regime classification based on sets of simulated Electrical Capacitance Tomography (ECT) data. Normalised ECT data have been used to separately train several MLPs employing various commonly used back-propagation learning algorithms, namely the Levenberg-Marquardt (LM), Quasi-Newton (QN) and Resilient-Backpropagation (RP), to classify the gas-oil flow regimes. The performances of the MLPs have been analysed based on their correct classification percentage (CCP). The results demonstrate the feasibility of using MLP, and the superiority of LM algorithm for flow regime classification based on ECT data.