Performance comparison of artificial neural networks and expert systems applied to flow pattern identification in vertical ascendant gas-liquid flows
Performance comparison of artificial neural networks and expert systems applied to flow pattern identification in vertical ascendant gas-liquid flows
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DOI：
10.1016/j.ijmultiphaseflow.2010.05.001
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发表时间：
2010-09-01
影响因子：
3.8
通讯作者：
Mastelari, N.
中科院分区：
文献类型：
--
作者：
Rosa, E. S.;Salgado, R. M.;Mastelari, N.
电阻率探头的瞬时读数是在向上垂直的空气-水混合物中获取的。信号被进一步处理以呈现这里用作客观流型指示器的统计矩和概率密度函数。通过目视观察，结合含气率轨迹和相关概率密度函数的分析，对73个实验段的流型进行了识别。将流型分为泡状流、球帽状流、弹状流、不稳定段塞流、半环状流和环状流。比较分析了人工神经网络、人工神经网络和专家系统在流型识别中的性能。所使用的人工神经网络有多层感知器、径向基函数和概率神经网络。具有单路和多路输出。性能通过基于实验观察的正确识别率来衡量。分析扩展到聚类算法，以帮助形成在神经网络和专家系统的学习阶段使用的知识库。下列聚类算法的性能：自组织映射。K-均值算法和模糊C-均值算法也用实验数据进行了检验。(C)2010爱思唯尔有限公司。保留所有权利。
Instantaneous readouts of an electrical resistivity probe are taken in an upward vertical air-water mixture. The signals are further processed to render the statistical moments and the probability density functions here used as objective flow pattern indicators. A series of 73 experimental runs have its flow pattern identified by visual inspection assisted by the analyses of the void fraction's trace and associated probability density function. The flow patterns are classified into six groups and labeled as: bubbly, spherical cap, slug, unstable slug, semi-annular and annular. This work compares and analyzes the performance of artificial neural networks, ANN, and expert systems to flow pattern identification. The employed ANNs are Multiple Layer Perceptrons, Radial Basis Functions and Probabilistic Neural Network. with single and multiple outputs. The performance is gauged by the percentage of right identifications based on experimental observation. The analysis is extended to clustering algorithms to assist the formation of knowledge base employed during the learning stages of the ANNs and expert systems. The performance of the following clustering algorithms: self organized maps. K-means and Fuzzy C-means are also tested against experimental data. (C) 2010 Elsevier Ltd. All rights reserved.