基于MO-PLP-ELM及电容层析成像的两相流流型辨识

doi:10.3969/j.issn.1000-1158.2021.03.12

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Abstract Identification of two-phase flow based on multi-objective optimized parallel layer perceptrons extreme learning machine (MO-PLP-ELM) and electrical capacitance tomography (ECT) is proposed. Firstly, the random training method is used to generate the training and testing sets for the studied seven two-phase flow regimes, which assures the representativeness of the samples. Secondly, the capacitance data of the sample are normalized. Finally, the MO-PLP-ELM algorithm is used for flow regime identification, and the results are compared with those of BP neural network, support vector machine, extreme learning machine algorithms and extreme learning machine with parallel layer perceptrons. The results show that the average recognition rate can reach 96.1% using MO-PLP-ELM, which is obviously higher than other algorithms.

Key words： metrology two-phase flow multi-objective optimization parallel layer percuseptrons extreme learning machine electrical capacitance tomography flow regime identification

Received: 05 August 2019 Published: 23 March 2021

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TB937

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	ZHANG Li-feng
	ZHU Yan-feng

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ZHANG Li-feng,ZHU Yan-feng. Identification of Two-phase Flow Based on MO-PLP-ELM and Electrical Capacitance Tomography[J]. Acta Metrologica Sinica, 2021, 42(3): 334-338.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.03.12 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I3/334

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