Description: 一种通过自组织竞争学习网络实现数据降维和可视化的单层神经网络模型。用此算法可以把输入空间的多维映射到低维的（一维或者二维）的离散网络上，并将保持相同性质的输入数据在映射到低维空间时的拓扑一致性。iris以及letter两个数据集进行分类-A competitive learning through self-organizing network for data dimensionality reduction and visualization of single-layer neural network model. Using this algorithm can be multi-dimensional input space is mapped to the low-dimensional (one-dimensional or two-dimensional) discrete network, and will remain the same as the nature of the input data is mapped to the low-dimensional space of topological consistency. iris as well as the letter the two data sets to classify Platform: |
Size: 1024 |
Author:军军 |
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Description: 运用神经网络的方法先将数据分类分析,然后再将未知类归类-The use of neural network analysis of data classification methods first, and then classify the unknown category Platform: |
Size: 5120 |
Author:qlovey |
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Description: 简单分类器 可用于基于神经网络的字母识别，适合初学者使用。
-Simple classifier based on neural network can be used to identify the letters, suitable for beginners to use. Platform: |
Size: 53248 |
Author:李敏 |
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Description: 利用神经网络竞争层给数据分类，程序后面有详细注释-The program classify data in neural network about Competitive layer, and detailed notes behind the program Platform: |
Size: 1024 |
Author:101 |
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Description: 基于BP神经网络的遥感图像分类代码。从样本中提取崇明岛东滩十种地物的光谱特征，并训练BP网络，再利用网络进行分类-BP neural network-based remote sensing image classification code. Extracted from samples of 10 kinds of Chongming Island, Dongtan features of the spectral characteristics and to train BP network, and then use the network to classify Platform: |
Size: 1413120 |
Author:李琛 |
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Description: 利用机器视觉系统代替人眼获取各项大米参数,再参照国标对其进行等级划分。在此基础上, 利用MATLAB软件的神经网络工具箱在数理统计基础上完成检测模型的构建,从而实现对未知大米外特性的评判,并为检测大米综合品质奠定了基础。-rice detection.At p resent, the evaluation method of the rice quality in China is still at the level of naked eye observation.
How to classify different varieties of rice through the quality parameters based on the adop ted evaluation criteria becomes
a new research top ic. In this paper, the machine vision- based method is used in studying the rice quality. On the basis
of the evaluation criteria, different kinds of rice are classified. And according to the usage of neural network, the detec2
tion model is established, so it can lay the foundation for the p rediction of the unknown kinds of rice in the future. Platform: |
Size: 8192 |
Author:wangjianshe |
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Description: 模糊聚类虽然能够对数据聚类挖掘，但是由于网络入侵特征数据维数较多，不同入侵类别间的数据差别较小，不少入侵模式不能被准确分类。本案例采用结合模糊聚类和广义神经网络回归的聚类算法对入侵数据进行分类。
-Although fuzzy clustering to cluster the data mining, but the characteristics of the network intrusion data more dimensions, different invasion was little difference between categories of data, many intrusion model can not be accurately classified. This case using fuzzy clustering and generalized regression neural network clustering algorithm to classify data on the invasion. Platform: |
Size: 25600 |
Author:si |
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Description: 基于模糊神经网络的多传感器信息融合，提出了一种简单、有效的分区算法来确定障碍物的距离和方位。采用BP神经网络对障碍物环境进行分类以及模式识别，为移动机器人的导航和避障提供了一种有效的方法。-Fuzzy neural network based multi-sensor information fusion, we propose a simple and effective algorithm to determine the partition barrier distance and direction. BP neural network to classify the environment of the obstacles and pattern recognition for mobile robot navigation and obstacle avoidance provides an effective method. Platform: |
Size: 261120 |
Author:wang |
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Description: 简单的实用的便捷的神经网络程序，有效的提高数据处理效率，有助于初学者的使用。-A simple and practical and convenient neural network program, effectively improve the efficiency of data processing Platform: |
Size: 1024 |
Author:yu |
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Description: MATLAB神经网络30例书课后附的案例源程序 BP神经网络的数据分类-语音特征信号分类-MATLAB neural network 30 cases of after-school book case attached source BP neural network data classification- the speech characteristic signal classification Platform: |
Size: 376832 |
Author:tongtong |
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Description: classify data (heart disease) by using artificial neural network (ANN) to 2 class and 5 class Platform: |
Size: 44032 |
Author:WENG0125 |
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Description: 搭建简单的BP神经网络，对数据进行分类，理解简单的神经网络搭建方法。(A simple BP neural network is built to classify data and understand simple neural network building method.) Platform: |
Size: 94208 |
Author:punpkin |
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Description: Motion recognition has received increasing attention in recent years owing to heightened demand for
computer vision in many domains, including the surveillance system, multimodal human computer
interface, and trac control system. Most conventional approaches classify the motion recognition task
into partial feature extraction and time-domain recognition subtasks. However, the information of motion
resides in the space-time domain instead of the time domain or space domain independently, implying that
fusing the feature extraction and classi cation in the space and time domains into a single framework is
preferred. Platform: |
Size: 910336 |
Author:nabill |
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