Lu, J.L. Chen, Applying the self-organization feature map (SOM) algorithm to AE-based tool wear monitoring in micro-cutting, Mechanical Sys- tems and Signal Processing 34 (2013) 353-366.Applying the self-organization feature map (SOM) algorithm to AE-based tool wear monitoring in micro-...
自组织映射(SOM)或自组织特征映射(SOFM)是一种类型的人工神经网络(ANN),其使用已训练的无监督学习以产生低维(通常为二维),离散的表示训练样本的输入空间,称为地图,因此是一种减少维数的方法。自组织映射与其他人工神经网络不同,因为它们应用竞争学习而不是纠错学习(例如具有梯度下降的反向传播)),从某种意义上说,...
Hebbian learning was employed to create the SOM in each map, by calculating the weight update based on the input signal. The difference between this implementation and MEMSORN is two-fold; (i) the learning in the SOM network is offline and is learned based on batches of data that are ...
Compared to the clustering algorithm of DAVID, these experimental results show a marked improvement in the accuracy of classification with the application of FCSOMs. FCSOMs can handle huge datasets and their complex classification problems, as each FCSOM (modeled for each function cluster) can be ...
(2013). A comparison study for intrusion database (Kdd99, Nsl-Kdd) based on self-organization map (SOM) artificial neural network. Journal of Engineering Science and Technology, 8(1), 107-119.Ibrahim, Laheeb M., Dujan T. Basheer, and Mahmod S. Mahmod. "A Comparison Study For ...
Here the self-organization property of one-dimensional Kohonen's algorithm in its 2k-neighbor setting with a general type of stimuli distribution and non-increasing learning rate is considered. A new definition of the winner is given, which coincides with the usual definition in implementations of ...
python3 train.py --model desom --dataset fmnist --map_size 20 20 Training generates several outputs: an image of the DESOM map to visualize the prototypes a graph of the model architecture a folder containing a log of training metrics and the model weights (by default,results/tmp) ...
A string-matching application shows the extension of the algorithm to cases where a scalar distance between input points is available. The map in the latter application, like a Kohonen map, exhibits local smoothness permitting shortcuts to the nearest-neighbor search 展开 ...
The SOM algorithm uses this matrix as input to obtain clusters of agents. These clusters reduce the search space, resulting in a relatively shea agent search time.中南大学学报(英文版)doi:10.1007/s11771-000-0015-yKelegamaCentDCCentLiuCentLHCentJQCentDimuthu C.Self Organization Map for Clustering...
The Self-Organizing Map (SOM) forms a nonlinear projection from a high-dimensional data manifold onto a low-dimensional grid. A representative model of some subset of data is associated with each grid point. The SOM algorithm computes an optimal collection of models that approximates the data ...