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Study on pattern recognition model based on principal component analysis and radius basis function neural network

机译:基于主成分分析和半径基函数神经网络的模式识别模型研究

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A pattern recognition model was proposed. Firstly, the theories of principal component analysis and radius basis function neural network were introduced. By the method of principal component analysis, the principal components influencing the pattern recognition were extracted. Based on the analysed results, the model of pattern recognition based on principal component analysis and radius basis function neural network was established. Then it was applied to classify 20 wear particles. And the accuracy of recognition reached 91.3%. The result indicates that this model could get faster speed and higher accuracy, and is worthy of further study and wide use.
机译:提出了一种模式识别模型。首先介绍了主成分分析和半径基函数神经网络的理论。通过主成分分析的方法,提取了影响模式识别的主成分。根据分析结果,建立了基于主成分分析和半径基函数神经网络的模式识别模型。然后将其应用于对20个磨损颗粒进行分类。识别精度达到91.3%。结果表明,该模型具有更快的速度和更高的精度,值得进一步研究和广泛应用。

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