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应用主成分分析对数据降维,将得到的数据用于概率神经网络训练,进行模式识别。对于一组新数据,先计算主成分得分,再输入训练好的概率神经网络,就会得到识别结果,即改组数据属于何种类别。-Principal component analysis of the data reduction, data will be obtained for the probabilistic neural network training, pattern recognition. For a new set of d
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概率神经网络的分类预测-基于PNN变压器故障诊断-Categories prediction probabilistic neural network- PNN Transformer Fault Diagnosis Based
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基于概率神经网络的采油机故障诊断,运行后能对采油机样本是否故障进行判断并能给出故障类型-Troubleshooting probabilistic neural network-based oil extraction machine, running on the extraction machine can sample and determine whether the fault can be given the type of fault
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基于概率神经网络的手写体数字识别,程序可以运行,会显示识别率百分比-Handwritten digit recognition based on probabilistic neural network, the program can run, it displays the percentage recognition rate
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PNN概率神经网络,用于分类,具体是基于PNN的变压器故障诊断-PNN probabilistic neural network is used to classify the transformer fault diagnosis based on PNN
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概率神经网络的分类预测-基于PNN变压器故障诊断-Classification prediction of probabilistic neural network based on PNN transformer fault diagnosis
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A probabilistic neural network (PNN) is a feedforward neural network, which was derived the Bayesian network and a statistical algorithm called Kernel Fisher discriminant analysis-A probabilistic neural network (PNN) is a feedforward neural network,
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概率神经网络的matlab源代码,可用于实现概率神经网络识别和判别数据类别。-Probabilistic neural network matlab source code, can be used to achieve probabilistic neural network identification and classification of data categories.
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用matlab实现的基于概率神经网络的手写体数字识别程序,这是一个概率神经网络的实际应用-Using matlab to achieve based on probabilistic neural network handwritten numeral recognition program, which is the practical application of a probabilistic neural network
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Probabilistic Neural Network for binary classification in python. Also using K-Folding technique in python.
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《MATLAB神经网络原理与实例精解》中chap13中的例子 基于概率神经网络的柴油机故障诊断-Diesel Engine Fault Diagnosis based probabilistic neural network- " MATLAB network principles and examples of fine nerve Solutions" in chap13 examples
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《MATLAB神经网络原理与实例精解》中chap13的例子 基于概率神经网络的手写体数字识别-" MATLAB network principles and examples of fine nerve Solutions" in the example chap13- Based Probabilistic Neural Network handwritten numeral recognition
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基于概率神经网络的柴油机故障诊断,使用MATLAB,希望帮助到大家-Diesel Engine Fault Diagnosis based on probabilistic neural network using MATLAB, we hope to help
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基于概率神经网络的手写数字识别,利用概率神经网络识别1-9的手写数字,matlab程序(Handwritten numeral recognition based on probabilistic neural network)
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概率神经网络的分类预测,基于PNN变压器故障诊断(Probabilistic neural network classification prediction based on PNN transformer fault diagnosis)
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采用概率神经网络进行变压器故障诊断,内含故障数据以及程序详尽程序注释(Using Probabilistic Neural Network for Transformer Fault Diagnosis, Containing Fault Data and Program Detailed Program Notes)
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概率神经网络(Probabilistic Neural Network)是由D.F.Speeht博士在1989年首先提出,是径向基网络的一个分支,属于前馈网络的一种。它具有如下优点:学习过程简单、训练速度快;分类更准确,容错性好等。从本质上说,它属于一种有监督的网络分类器,基于贝叶斯最小风险准则。(Probabilistic neural network was first proposed by Dr. D.F.Speeht in 1989. It is a branch of radial
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该PDF共有30个MATLAB神经网络的案例,包括BP、RBF、SVM、SOM、Hopfield、LVQ、Elman、小波等神经网络;还包含PSO(粒子群)、灰色神经网络、模糊网络、概率神经网络、遗传算法优化等内容。本PDF作为本科毕业设计、研究生项日设计、博士低年级课题设计参考书籍,同时对广大科研人员也有很高的参考价值。(The PDF has a total of 30 MATLAB in the case of neural networks, including BP, RBF, SVM
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态势要素获取作为整个网络安全态势感知的基础,其质量的好坏将直接影响态势感知系统的性能。针对态势要素不易获取问题,提出了一种基于增强型概率神经网络的层次化框架态势要素获取方法。在该层次化获取框架中,利用主成分分析(PCA)对训练样本属性进行约简并对特殊属性编码融合处理,将其结果用于优化概率神经网络(PNN)结构,降低系统复杂度。以PNN作为基分类器,基分类器通过反复迭代、权重更替,然后加权融合处理形成最终的强多分类器。实验结果表明,该方案是有效的态势要素获取方法并且精确度达到95.53%,明显优于
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为了真实有效地提取网络安全态势要素信息,提出了一种基于增强型概率神经网络的层次化框架态势要素获取方法。在该层次化态势要素获取框架中,根据Agent节点功能的不同,划分为不同的层次。利用主成分分析(Principal Component Analysis, PCA)对训练样本属性进行约简并对特殊属性编码融合处理,按照处理结果改进概率神经网络(Probabilistic Neural Network, PNN)结构,以降低系统复杂度。然后以改进的PNN作为基分类器,结合自适应增强算法,通过基分类器反
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