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A novel method for disturbance detection, localization and pattern recognition in signal images

机译:信号图像中干扰检测,定位和模式识别的一种新方法

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A new approach to pattern recognition and classification of non-stationary power signal is presented in this paper. In the proposed work visual localization and detection of non-stationary power signals are achieved using Image processing and its pattern is recognized and classified by MLP neural network algorithm. Also disturbance localization and classification of the signal, is done once with image processing and one more time with neural network and the results are compared with each other. In MLP neural network method we tried to reduce the disturbance localization errors of image processing method. Various non-stationary power signals are processed through image processing stage to generate property charts of the signal for extracting relevant features for pattern classification. The extracted features are clustered using MLP Neural Network algorithm to refine the cluster centers.
机译:本文介绍了一种新的模式识别和分类的模式识别和分类。 在所提出的工作视觉本地化和使用图像处理实现非静止功率信号的检测,并且通过MLP神经网络算法识别和分类其图案。 也是信号的扰动定位和分类,通过图像处理进行一次,并且再次使用神经网络,并且结果彼此比较。 在MLP神经网络方法中,我们尝试降低图像处理方法的干扰定位误差。 通过图像处理阶段处理各种非静止功率信号,以生成用于提取模式分类的相关特征的信号的属性图。 通过MLP神经网络算法群集提取的特征来优化群集中心。

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