Indicator diagram identification based on ART2 neural network and features of moment invariant
Indicator diagram identification based on ART2 neural network and features of moment invariant
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DOI：
10.1109/cecnet.2012.6202189
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发表时间：
2012-04
期刊：
影响因子：
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通讯作者：
Yuehui Peng;Shuguang Liu;Yanyan Zhang
中科院分区：
文献类型：
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作者：
Yuehui Peng;Shuguang Liu;Yanyan Zhang
示功图是分析抽油机井下工况的重要依据。目前主要依靠人工判断来识别指标图，不仅对人们的经验要求较高，而且花费较多的时间和精力。 ART2神经网络对于任意序列的连续或二值模式，具有快速稳定的学习能力。它克服了前馈神经网络学习速度慢等缺点；陷入局部最小值并轻松洗掉先前学到的信息。矩不变量可以体现二维图形的关键特征；它还具有旋转、拉伸不变性、抗干扰性强等特点，可用于模式识别的特征提取。本文主要基于ART2神经网络和矩不变量来识别不同工况下的指标图。识别结果表明，ART2神经网络在指示图识别过程中具有快速、稳定、准确率高等优点。
The indicator diagram is an important basis to analyze the downhole conditions of the pumping unit. Currently, it is mainly depends on artificial judgment to identify the indicator diagram, that not only needs higher requirements of the peoples' experience, but also spends much more time and energy. ART2 neural network for an arbitrary sequence of continuous or binary mode, has the ability of fast and stable learning. It overcomes the shortcomings of the feed-forward neural networks, such as learning slowly; falling into the local minimum and washing away previously learned information easily. Moment invariants can reflect the key characteristics of two-dimensional graphics; it also owns the traits of rotation, stretching invariance and strong anti-interference, etc. It can be used for the feature extraction of pattern recognition. This paper mainly based on ART2 neural network and the moment invariant to identify the indicator diagrams in different working conditions. The recognition result shows that the ART2 neural network owns many advantages such as the fast, stable and high accuracy performance in the indicator diagram recognition process.