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基于MATLAB的主元分析,可用于信号特征提取及模式识别-the main element analysis, signal can be used for feature extraction and pattern recognition
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這是一個類神經網路中倒傳遞類神經網路，應用於圖形識別上，可以把兩個不同分類的點，藉由BP找出他的分割線進行區分-This is a type of neural network back-propagation neural network applied to pattern recognition, it can be classified into two different points, by BP to find his distinction between partition l
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调用GDAL库实现模式识别的K均值和ISODATA算法，对图像的分类-Call GDAL library implementation of K-means and ISODATA algorithms pattern recognition, image classification
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通过使用ISODATA算法实现对图像的模式识别分类-Image pattern recognition classification using ISODATA algorithm
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支持向量机模型用于预测分析效果显著，它在解决小样本、非线性及高维模式识别中表现出许多特有的优势，并能够推广应用到函数拟合等其他机器学习问题中-Support vector machine model is used to predict significant effect in solving small sample, nonlinear and high dimensional pattern recognition exhibit many unique advantages, and
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BP神经网络用与实现不同故障的分类，对复杂信号模式识别是一种很有效的方法。-BP neural network with the classification of different faults and implementation of complex signal pattern recognition is a very effective method.
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数据来源：直接与天软数据连接，可测试多品种；
功能：根据模式识别方法判别商品及股指期货标的涨跌方向；
-Source: soft data connection directly with the day, you can test many varieties function: discrimination based on pattern recognition methods underlying commodity and stock index futures Change
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c均值算法，模式识别，可以运行的正确程序。-C means algorithm, pattern recognition, can correct program.
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基于最小二乘支持向量机的模式识别，特征向量为滚动轴承的能量百分比和峭度系数-Pattern recognition based on least squares support vector machine (SVM), the percentage of energy eigenvector for rolling bearing and kurtosis coefficient
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模式识别的相关内容，MATLAB程序代码-Pattern recognition of the related content, MATLAB code
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此例可采用BP神经网络算法用于设备故障类型的模式识别程序。 本次为变压器故障（均为低压测故障）类型的识别。-This example can be used BP neural network algorithm for pattern recognition procedures for equipment fault type. The identification of the type of transformer fault (both low pressure fault detec
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