The accurate identification of flow regimes is important for the operation and design of interrelated instruments. Based on a large amount of experimental data, wavelet transform, chaos theory, neural network and date fusion are used in flow regime identification.
本文在大量实验数据基础上,将小波变换、混沌理论、神经网络和数据融合技术应用到流型识别中,从理论和实验两个方面,系统地探讨了流型的神经网络智能识别方法。
参考来源 - 基于小波和混沌理论的气液两相流流型智能识别方法·2,447,543篇论文数据，部分数据来源于NoteExpress
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