Description: DNA分类，包含三中模式识别经典算法的实现：K紧邻，BP神经网络，概率神经网络。-DNA classification, which includes three classic pattern recognition algorithm to achieve : K borders, BP neural networks, probabilistic neural network. Platform: |
Size: 192512 |
Author:郭瑞杰 |
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Description: RBF神经网络的K均值算法，C程序的，供大家参考！-RBF neural network algorithm mean K, C procedures, for your reference! Platform: |
Size: 29696 |
Author:hxm |
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Description: BP神经网络的C语言实现 BP神经网络解决异或问题 canny源代码 HMM的C语言实现 isodata K-MEANS 车牌识别系统 矢量量化的C语言实现 -Neural Network C language BP neural network solution differences or problems canny source HMM C language isodata K-MEANS License Plate Recognition System Vector Quantization C Language Platform: |
Size: 794624 |
Author:liang |
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Description: 一个基于K均值聚类的ＲＢＦ神经网络，注释写的很明白，有不明白的地方可以发邮件问我。-a K-means clustering based on the RBF neural network, notes written very well, did not understand the local mail can ask me. Platform: |
Size: 2048 |
Author:bruce |
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Description: K均值法，神经网络很有用
不同于BP神经网络的算法-K-means, useful neural network is different from the BP neural network algorithm Platform: |
Size: 208896 |
Author:王昱 |
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Description: 神经网络中的K均值聚类算法II：
1.KMIn为输入数据文本，其中，第一个参数为所要聚类点个数，第二个参数为聚类点的维数,第三个参数为所要求聚类的个数
2.KM2OUT为经过K均值聚类算法II计算后得到的结果-Neural network in K-means clustering algorithm II: 1.KMIn input data for the text, of which the first parameter to be the number of clustering points, the second parameter is the dimension of clustering points, the third parameter for the clustering the number of requests for 2.KM2OUT after K-means clustering algorithm II calculation results Platform: |
Size: 277504 |
Author:blue8202 |
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Description: 文中设计了一个3层径向基神经网络(RBFN)用于对企业的5项评价指标进行聚类分析，并与蚁群算法做了比较分析。RBFN由输入层
到隐含层采用传统的K一均值算法，隐含层到输出层通过“模2递减”学习速率的BP学习；蚁群算法根据信息素的分配能够自动调整收索
路径，从而达到数据自动聚类的目的。结果表明，与蚁群算法相比，改进RBFN具有快速收敛、自动识别奇异样本的优点，而蚁群算法
无须教师学习，并能够达到全局最优。-In this paper, we designed a 3-layer RBF neural network (RBFN) for the 5-to-business evaluation indicators cluster analysis and ant colony algorithm has done a comparative analysis. RBFN from input layer to hidden layer using the traditional K-means algorithm, hidden layer to output layer through the Mode 2 decreasing learning rate of BP learning ant colony algorithm based on pheromone can automatically adjust the allocation of land Faso path, thereby to achieve the purpose of automatic data clustering. The results showed that compared with the ant colony algorithm to improve the RBFN has a fast convergence, automatic identification of singular advantage of the sample, while the ant colony algorithm do not need teachers to learn and be able to reach the global optimum. Platform: |
Size: 165888 |
Author:luhui |
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Description: Image thresholding has played an important role in image segmentation. In this paper, we present a novel spatially weighted fuzzy c-means (SWFCM) clustering algorithm for image thresholding. The algorithm is formulated by incorporating the spatial neighborhood information into the standard FCM clustering algorithm. Two improved implementations of the k-nearest neighbor (k-NN) algorithm are introduced for calculating the weight in the SWFCM algorithm so as to improve the performance of image thresholding. Platform: |
Size: 293888 |
Author:silviudog |
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Description: Neural Network C language BP neural network solution differences or problems canny source HMM C language isodata K-MEANS License Plate Recognition System Vector Quantization C Language Platform: |
Size: 168960 |
Author:mjdkadh |
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Description: 聚类算法：聚类分析是指事先不了解一批样品中的每一个样品的类别
基于k均值聚类学习算法的rbf神经网络实现-Clustering algorithm: cluster analysis is the prior knowledge of each batch of samples in the sample of category learning algorithm based on k means clustering of rbf neural network Platform: |
Size: 1024 |
Author:xw |
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Description: EEG signals classification using the K-means clustering and a multilayer perceptron neural network model Platform: |
Size: 2897920 |
Author:ALi |
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Description: RBF（径向基神经网络）网络是一种重要的神经网络，RBF网络的训练分为两步，第一步是通过聚类算法得到初始的权值，第二步是根据训练数据训练网络的权值。RBF权值的初始聚类方法较为复杂，比较简单的有K均值聚类，复杂的有遗传聚类，蚁群聚类等，这个RBF网络的程序是基于K均值聚类的RBF代码。(RBF (radial basis function network) is an important neural network. The training of RBF network is divided into two steps. The first step is to get the initial weight by clustering algorithm, and the second step is to train the weight of the network according to training data. The initial clustering method of RBF weights is relatively complex. There are relatively simple K mean clustering, complex genetic clustering, ant colony clustering, etc. this RBF network is based on K means clustering RBF code.) Platform: |
Size: 6144 |
Author:老外
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Search - k-means neural network