| 邓 勇,蒋 田,赖治屹.基于 DBN 和 LSSVM 的管道气体压力检测方法研究[J].电子测量与仪器学报,2021,35(6):199-205 |
| 基于 DBN 和 LSSVM 的管道气体压力检测方法研究 |
| Research on pipeline gas pressure detection method based on DBN and LSSVM |
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| DOI： |
| 中文关键词: 管道 气体压力 深度置信网络 最小二乘法支持向量机 |
| 英文关键词:pipeline gas pressure deep belief network least square support vector machine |
| 基金项目:四川省科技支撑计划项目（2017FZ0033）资助 |
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| 中文摘要: |
| 针对当前管道气体压力无损检测困难的问题,结合超声波反射测压原理,提出了深度置信网络(DBN)提取超声回波幅
值特征的最小二乘法支持向量机(LSSVM)管道气体压力检测方法。 首先,通过 DBN 网络中的受限玻尔兹曼机(RBM)无监督
逐层学习提取特征;其次,通过标签层进行有监督的误差反向传播调节优化 DBN 各层 RBM 参数;最后,将优化后 DBN 网络提
取到的特征信号输入训练好的 LSSVM 完成气体压力的识别。 设计相关实验得到超声波数据进行模型测试,结果表明,DBNLSSVM 压力识别模型的压力识别平均相对误差为 0. 635 7%,低于 DBN-BP 模型的平均相对误差(1. 802 6%),能够较好地完成
对管道气体的压力检测工作。 |
| 英文摘要: |
| Aiming at the current difficulty in nondestructive testing of pipeline gas pressure, combined with the principle of ultrasonic
reflection pressure measurement, a deep belief network (DBN) extraction of ultrasonic echo amplitude characteristics is proposed, which
is least square support vector machine ( LSSVM) pipeline gas pressure detection method. First, the features are extracted through
unsupervised layer-by-layer learning of the restricted Boltzmann machine (RBM) in the DBN network. Secondly, the supervised error
back propagation adjustment is performed through the label layer to optimize the RBM parameters of each layer of the DBN. Finally,
input the characteristic signal extracted by the optimized DBN network into the trained LSSVM to complete the gas pressure recognition.
Design related experiments to obtain ultrasonic data for model testing. The results show that the average relative error of pressure
recognition of the DBN-LSSVM pressure recognition model proposed in this paper is 0. 635 7%, which is lower than the average relative
error of the DBN-BP model (1. 802 6%), which is better complete the pressure detection of the pipeline gas. |
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