Description: 本文针对SLAM算法中数据关联过程，提出了一种简单易行的改进方法，将欧氏距离与马氏距离结合用于数据关联。算法不必计算地图所有特征与所有量测之间的马氏距离，而是首先利用相对简单的欧氏距离计算缩小了待关联特征的搜寻范围。利用人工合成数据的仿真结果表明，改进后的数据关联方法可以大幅减少系统计算量，提高关联效率，且不会造成错误关联的增加。-This article SLAM algorithm for data association process, a simple method to Euclidean distance combined with the Mahalanobis distance for data association. Algorithms do not have to calculate all the characteristics of the map with all measurements between the Mahalanobis distance, but the first to use a relatively simple calculation of Euclidean distance to be associated characteristics of narrowing the search. The use of synthetic data simulation results show that the improved methods of data association can significantly reduce the system to calculate the volume and improve the efficiency of association, and will not lead to errors associated with an increase. Platform: |
Size: 13312 |
Author:liancb |
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Description: Kohonen神经网络算法工作机理为：网络学习过程中，当样本输入网络时，竞争层上的神经元计算输入样本与竞争层神经元权值之间的欧几里德距离，距离最小的神经元为获胜神经元。调整获胜神经元和相邻神经元权值，使获得神经元及周边权值靠近该输入样本。通过反复训练，最终各神经元的连接权值具有一定的分布，该分布把数据之间的相似性组织到代表各类的神经元上，使同类神经元具有相近的权系数，不同类的神经元权系数差别明显。需要注意的是，在学习的过程中，权值修改学习速率和神经元领域均在不断较少，从而使同类神经元逐渐集中。-Kohonen neural network algorithm for the working mechanism: the network learning process, when the samples enter the network, the competitive layer of neurons on the calculation of input samples and competitive layer neurons Euclidean distance between the weights from the smallest neurons Winning neuron. Adjust the winning neuron and neighboring neurons weights to gain weight and peripheral neurons close to the input samples. Through repeated training, and ultimately the connection weights of neurons with a certain distribution, the distribution of the similarity between the data to representatives of organizations of various types of neurons, so that similar neurons have similar weights, different types of nerve Element obviously different weights. Note that, in the learning process, the right to modify the value of the field of learning rate and neurons were constantly low, so that the same neurons gradually concentrated. Platform: |
Size: 87040 |
Author:李芳 |
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Description: 在matlab环境中自动识别连通区域的大小,计算两个矩阵之间的欧氏距离，时间序列数据分析中的梅林变换工具。- Automatic identification in the matlab environment the size of the connected area, Calculation of the Euclidean distance between the two matrices, Time series data analysis Mellin transform tool. Platform: |
Size: 5120 |
Author:nen |
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Description: 计算两个矩阵之间的欧氏距离，使用matlab实现智能预测控制算法，现代信号处理中谱估计在matlab中的使用。- Calculation of the Euclidean distance between the two matrices, Use matlab intelligent predictive control algorithm, Modern signal processing used in the spectral estimation in matlab. Platform: |
Size: 6144 |
Author:张争龙 |
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Description: 连续相位调制信号(CPM)产生，在matlab环境中自动识别连通区域的大小,计算两个矩阵之间的欧氏距离。- Continuous phase modulation signal (CPM) to produce, Automatic identification in the matlab environment the size of the connected area, Calculation of the Euclidean distance between the two matrices. Platform: |
Size: 8192 |
Author:henghui |
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Description: Matlab实现界面友好，在matlab环境中自动识别连通区域的大小,计算两个矩阵之间的欧氏距离。- Matlab to achieve user-friendly, Automatic identification in the matlab environment the size of the connected area, Calculation of the Euclidean distance between the two matrices. Platform: |
Size: 6144 |
Author:bnivwss |
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Description: 在matlab环境中自动识别连通区域的大小,仿真效率很高的，计算两个矩阵之间的欧氏距离。- Automatic identification in the matlab environment the size of the connected area, High simulation efficiency, Calculation of the Euclidean distance between the two matrices. Platform: |
Size: 6144 |
Author:luipunjanlun |
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Description: Calculation of the Euclidean distance between the two matrices, Course designed to prepare the matlab program code, In matlab R2009b debugging through. Platform: |
Size: 6144 |
Author:ganqanhan
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Description: Automatic identification in the matlab environment the size of the connected area, Virtual power wireless sensor network coverage, Calculation of the Euclidean distance between the two matrices. Platform: |
Size: 39936 |
Author:王辉涛
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Search - Euclidean distance calculation in matlab