Time behavior of topological ordering in self-organizing feature mapping

1997 ◽  
Vol 77 (6) ◽  
pp. 419-427 ◽  
Author(s):  
R. Der ◽  
M. Herrmann ◽  
T. Villmann
Keyword(s):  
Time Behavior ◽  
Feature Mapping ◽  
Self Organizing

scholarly journals Early warning of debris flow using optimized self-organizing feature mapping network

2020 ◽  
Vol 20 (7) ◽  
pp. 2455-2470
Author(s):  
Xuedong Wang ◽  
Cui Wang ◽  
Chaobiao Zhang
Keyword(s):  
Neural Network ◽  
Debris Flow ◽  
Early Warning ◽  
Rainfall Threshold ◽  
Liaoning Province ◽  
Feature Mapping ◽  
Threshold Method ◽  
Flow Area ◽  
Competitive Neural Network ◽  
Self Organizing

Abstract Early warning of debris flow is one of the core contents of disaster prevention and mitigation work for debris flow disasters. There are few early warning methods based on the combination of rainfall threshold and geological environment conditions. In this paper, we presented an early warning method for debris flow based on the infinite irrelevance method (IIM) and self-organizing feature mapping (SOFM), and applied it to Liaoning Province, China. The proposed model consisted of three stages. Firstly, eight geological environmental conditions and two rainfall-inducing conditions were selected by analyzing the factors affecting the development of debris flow in the study area, and the rainfall threshold for debris flow outbreak was 150 mm. Secondly, the correlation between various factors was analyzed by IIM, which prevented the blindness of parameter selection and improved the prediction accuracy of the model. Finally, SOFM was employed to predict the test data. Experimental results showed that the IIM-SOFM model had a strong early warning ability. When 25 samples of low-frequency debris flow area were selected, the accuracy rate of the IIM-SOFM model with optimized network structure parameters was 100%, which it was obviously superior to the rainfall threshold method, BP neural network and competitive neural network. Consequently, it is feasible to use the IIM-SOFM model for early warning of debris flow, outperforming traditional machine learning methods.

Classification of whale song units using a self‐organizing feature mapping algorithm

1996 ◽  
Vol 100 (4) ◽  
pp. 2644-2644 ◽  
Author(s):  
Nicholas Mitsakakis ◽  
Robert Fisher ◽  
Ashley Walker
Keyword(s):  
Feature Mapping ◽  
Mapping Algorithm ◽  
Self Organizing

The Fault Diagnosis of Automotive Airbag Assembly Process Based on Self-Organizing Feature Mapping Network SOM

2011 ◽  
Vol 128-129 ◽  
pp. 1101-1104
Author(s):  
De Jiang Zhang ◽  
Niao Na Zhang ◽  
Ke Wei Liu
Keyword(s):  
Fault Diagnosis ◽  
Excitatory Neuron ◽  
Local Optimum ◽  
Assembly Process ◽  
Feature Mapping ◽  
Field Function ◽  
Self Organizing

Automotive airbag assembly process is complex and nonlinear, and one of its characteristics is that the accuracy of making the threshold comparison for fault diagnosis using field multi-sensor measured value is not high,. In this article, adopt self-organizing feature mapping network SOM to realize the fault diagnosis of automotive airbag assembly process, constitute the field function of SOM through wavelet functions, form sub-excitatory neuron to update weights, avoid SOM local optimum, so improve the accuracy of fault diagnosis of automotive airbag assembly process.

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