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首页> 外文期刊>WSEAS Transactions on Signal Processing >Guided Waves Damage Identification in Beams with Test Pattern Dependent Series Neural Network Systems
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Guided Waves Damage Identification in Beams with Test Pattern Dependent Series Neural Network Systems

机译：依赖于测试图案的系列神经网络系统对光束中的导波损伤进行识别

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摘要

In regression neural networks for pattern recognition of preprocessed guided waves signals in beams, a trained network produced large errors when identifying a test pattern not found in the training set. To improve the accuracy of results, a new neural network procedure was introduced where progressive training was performed in a series combined network with the integration of a weight-range selection (WRS) technique that was dependent on the test pattern. The WRS method was applied for a supervised multi-layer perceptron operating with one hidden layer of neurons and trained using a backpropagation algorithm. The system was able to achieve average predictions accurate to 2.5% and 7.8% of the original training range sizes for the damage location and depth, respectively, while the WRS provided up to 13.9% improvement compared to equivalent conventional neural networks.
机译：在用于对波束中的预处理导波信号进行模式识别的回归神经网络中，经过训练的网络在识别训练集中未找到的测试模式时会产生较大的误差。为了提高结果的准确性，引入了一种新的神经网络程序，该程序在一系列组合网络中进行逐步训练，并结合了取决于测试模式的权重范围选择（WRS）技术。 WRS方法应用于带有一层神经元隐藏层的有监督的多层感知器，并使用反向传播算法进行训练。该系统能够实现平均预测，精确度分别为损伤位置和深度的原始训练范围大小的2.5％和7.8％，而WRS与等效的传统神经网络相比，可提供高达13.9％的改进。

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