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Description: SVM分类器，用于对多维采样点进行分类。可根据类别数修改分类器，我们的模式识别作业。-SVM classifier, multi-dimensional sampling points used for classification. Can be modified according to the number of classification categories, and our pattern recognition operation.
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Size: 5120 |
Author: 王兵 |
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Description: pnn分类器算法，用MATLAB源码，可以进行分类。-pnn classifier algorithm, using MATLAB source code can be classified.
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Size: 1024 |
Author: wasd |
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Description: 基于不变矩理论，提出一种应用概率神经网络作为识别器的车牌汉字识别技术。利用Pseudo-Zernike 矩特征的旋转不变性和良好
的抗噪性能，将其作为车牌汉字识别的特征矢量，结合Pseudo-Zernike 矩的快速算法和概率神经网络识别器快速学习和识别的性能，可适
应实时环境下所获取的车牌汉字灰度图像的识别，具有较高的准确率，实验结果表明了该方法的有效性。-】This paper presents a novel approach based on Pseudo-Zernike Invariant Moments(PZIM) and Probabilistic Neural Network(PNN) to
recognize license plate Chinese characters. The approach makes better use of the rotation invariant and good anti-noise performance of
Pseudo-Zernike moments and quick learning rate of PNN, and thus provides a real-time recognition of gray character images by utilizing
Pseudo-Zernike moments as feature vectors and Probabilistic Neural Network as classifier. Numeral experiment confirms that it is an effective way
to classify license plate Chinese characters.
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Size: 1236992 |
Author: ll |
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Description: 概率神经网络 分类预测 基于PNN变压器故障 诊断-Probabilistic neural network classifier prediction Transformer Fault Diagnosis Based on PNN
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Size: 2048 |
Author: guanyouyuan |
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Description: 该压缩包包含了pnn等分类器的源程序，用C++实现-The archive contains the source pnn other classifier, using C++,
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Size: 64512 |
Author: 方梦兰 |
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Description: 各种分类算法，包括KNN，PNN，bayes_classifier等等。-various classifier
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Size: 15462400 |
Author: tlj |
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Description: 对植物的分类研究已经突破了单纯从植物细胞及化学遗传成分的角度去鉴 定植物种类的方法, 可以综合应用图像处理技术和模式识别技术 , 辅以图像获取设备实现对植物的快速识别 。 为 此 , 精心选取了植物叶片图像的典型形状特征 , 构成了叶片识别的特征向量, 然后用概率神经网络 (P NN)作为分类 器 , 对样本进行训练。 实验结果证明 , 针对少量常见的植物叶片图像, PNN与 BP神经网络相比有更好的识别效率 -Research on the classification of the plant has been broke through the simple the Angle of plant cell and chemical genetic component to guide The method of plant species, can be integrated application of image processing and pattern recognition technology, supplemented by image acquisition device to realize fast recognition of plants.For this, carefully the typical shape characteristics of the plant leaf image, form the leaf recognition feature vector, then using probabilistic neural network (NN) P as a classifier, the training samples.The experimental results show that for a few common plant leaf image, PNN compared with BP neural network has better recognition efficiency
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Size: 300032 |
Author: hahah |
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Description: 随着计算机技术的飞速发展 , 对植物的分类研究已经突破了单纯从植物细胞及化学遗传成分的角度去鉴 定植物种类的方法, 可以综合应用图像处理技术和模式识别技术 , 辅以图像获取设备实现对植物的快速识别 。 为 此 , 精心选取了植物叶片图像的典型形状特征 , 构成了叶片识别的特征向量, 然后用概率神经网络 (P NN)作为分类 器 , 对样本进行训练。 实验结果证明 , 针对少量常见的植物叶片图像, PNN与 BP神经网络相比有更好的识别效率 。-With the rapid development of computer technology, the research on the classification of the plant has been broke through the simple the Angle of plant cell and chemical genetic component to guide The method of plant species, can be integrated application of image processing and pattern recognition technology, supplemented by image acquisition device to realize fast recognition of plants.For this, carefully the typical shape characteristics of the plant leaf image, form the leaf recognition feature vector, then using probabilistic neural network (NN) P as a classifier, the training samples.The experimental results show that for a few common plant leaf image, PNN compared with BP neural network has better recognition efficiency
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Size: 300032 |
Author: blwang |
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Description: 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 basis networks and belongs to a feedforward network. It has the following advantages: the learning process is simple, the training speed is fast, the classification is more accurate, and the fault tolerance is good. Essentially, it belongs to a supervised network classifier based on the Bayes minimum risk criterion.)
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Size: 5120 |
Author: gahuan
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Description: 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%，明显优于文中其他算法，有较好的泛化能力。(As the basis of the whole network security situation awareness, the quality of situation elements extraction will directly affect the performance of the situation awareness system. To solve the problem that the situation element is difficult to extract, we propose a method to extract the hierarchical frame situation elements based on the enhanced probabilistic neural network. In the hierarchical access frame, we use the principal component analysis (PCA) to reduct the training sample attribute and to process the special attribute encoding fusion. The result can be used to optimize the structure of the probabilistic neural network (PNN) and reduce the system complexity. Take PNN as the base classifier to form the final strong classifier by repeated iteration, weight replacement and weighted fusion. The experimental results show that the scheme is an effective method to obtain the situation factors and its accuracy is 95.53%,which is significantly better than other algorithms.)
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Size: 1213440 |
Author: 莫言婷婷
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Description: 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，通过基分类器反复迭代、样本权重更新，最后加权融合处理形成最终的强多分类器。实验结果表明，本文模型较文中其他几种方法具有较高的获取准确率和良好的泛化能力。(Firstly, in order to extract the information of network security situation accurately and effectively, a hierarchical frame feature acquisition method based on enhanced probabilistic neural network is proposed. According to different functions of Agent node, the hierarchical feature acquisition framework is divided into different levels. The principal component analysis (PCA) is used to reduce the training sample attributes and the special attribute encoding fusion. The result can be used to optimize the structure of the probabilistic neural network (PNN) so as to reduce the system complexity. Then, the improved PNN is used as the base classifier. Combined with the adaptive enhancement algorithm, the final strong classifier is formed through repeated iteration, weight replacement and weighted fusion. The experimental results show that the proposed model achieve higher accuracy and better generalization ability than other methods.)
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Size: 98304 |
Author: 莫言婷婷
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Description: 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。(The rate neural network, first proposed in 1989, is a branch of the RBF network and is one of the feedforward networks. It has the following advantages: the learning process is simple, the training speed is fast, the classification is more accurate, the fault tolerance is good, and so on. In essence, it belongs to a supervised network classifier based on Bayesian minimum risk criteria.)
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Size: 46080 |
Author: 哼哼1214
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