Description: 基于GMM的概率神经网络PNN具有良好的泛化能力，快速的学习能力，易于在线更新,并具有统计学的贝叶斯估计理论基础，已成为一种解决像说话人识别、文字识别、医疗图像识别、卫星云图识别等许多实际困难分类问题的很有效的工具。而且PNN不但具有GMM的大部分优点，还具有许多GMM没有的优点，如强鲁棒性，需要更少的训练语料，可以和其他网络其他理论无缝整合等。-GMM based probabilistic neural network PNN good generalization ability, the ability to learn fast, easy online updates, and with the Bayesian statistical theory based on estimates, and has become a solution as speaker recognition, text recognition, medical image recognition, satellite images and other real recognition when difficulties classification of very effective tool. But GMM PNN is not only the most advantages, but also has many advantages GMM not as strong robustness, require less training corpus, and other networks to other theories, such as seamless integration. Platform: |
Size: 7168 |
Author:姜正茂 |
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Description: lvq 算法 关于读取一个手写字体的数据库的代码的问题[图像图形-Identification of data sets, can be used as, the classification of experimental data. Category: Pattern Recognition Platform: |
Size: 1024 |
Author:zbgui |
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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. Platform: |
Size: 5120 |
Author:王兵 |
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Description: 三组二维输入向量P，以及其相对应的三个类别TC，目的是构建一个PNN网络对输入向量进行正确分类。-Three sets of two-dimensional input vector P, and their corresponding three categories TC, aimed at building a PNN network correct classification of input vectors. Platform: |
Size: 1024 |
Author:陈瑶 |
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Description: The code implements a probabilstic Neuraol network for classification problems trained with a Leave One Out Cross Validation Scheme in Matlab (version 7 or above). The following toolboxes are required: statidtics, optimization and neural networks. Platform: |
Size: 33792 |
Author:Alfredo/Passos |
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Description: pnn神经网络算法，通过训练后，可实现对电力变压器故障的分类-pnn neural network algorithm, by training, can be realized on the power transformer fault classification Platform: |
Size: 2048 |
Author:michael |
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Description: 结合DCT和概率神经网络进行人脸识别。先利用DCT提取特征，然后利用PNN分类，在ORL人脸库上测试效果不错。-The combination of DCT and probabilistic neural network for face recognition. First DCT Feature Extraction, and then use a PNN classification, good test results on the ORL face database. Platform: |
Size: 1024 |
Author:尹贺峰 |
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Description: 针对变压器的故障诊断，通过概率神经网络对其进行分类。运行程序后，网络输出值和实际值非常接近，很好的实现了对变压器的故障分类。-For the transformer fault diagnosis, we use the probability neural network to classify it. After running the program, the network output value and the actual value is very close, which achieves the good classification of transformer fault . Platform: |
Size: 2048 |
Author:kasino |
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Description: 《MATLAB神经网络30个案例分析》中的第19个例子，案例19 概率神经网络的分类预测-基于PNN变压器故障诊断。希望对大家有一定的帮助！-The MATLAB neural network analysis of 30 cases of 19 example, 19 cases of probabilistic neural network classification prediction based on PNN transformer fault diagnosis. Hope to have certain help to everybody!
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Size: 3072 |
Author:杨飞 |
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Description: 概率神经网络，经过调试可用，可进行扩展，可用模式分类。-probabilistic neural network, after debugging is available, can be extended，it can be used for pattern classification。 Platform: |
Size: 1024 |
Author:赵小志 |
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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.) Platform: |
Size: 5120 |
Author:gahuan
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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.) Platform: |
Size: 46080 |
Author:哼哼1214
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