Gentrification Without Exclusion? A SOM Neural Network Investigation on the Isola District in Milan

2012 ◽  
pp. 127-149
Author(s):  
Lidia Diappi ◽  
Paola Bolchi ◽  
Luca Gaeta
Keyword(s):  
Neural Network ◽  
Som Neural Network

Research on Semantic Web Services Discovery Mechanism Based on Kernel Batch SOM Neural Network

2011 ◽  
Vol 33 (6) ◽  
pp. 1307-1313
Author(s):  
Lei Chen ◽  
Geng Yang ◽  
Ying-zhou Zhang ◽  
Yan-li Chen
Keyword(s):  
Neural Network ◽  
Web Services ◽  
Semantic Web ◽  
Semantic Web Services ◽  
Som Neural Network

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

scholarly journals Application of SOM neural network in clustering

2009 ◽  
Vol 02 (08) ◽  
pp. 637-643 ◽  
Author(s):  
Soroor Behbahani ◽  
Ali Moti Nasrabadiv
Keyword(s):  
Neural Network ◽  
Som Neural Network

scholarly journals Alteration mineral mapping with ASTER data by integration of coded spectral ratio imaging and SOM neural network model

2014 ◽  
Vol 23 ◽  
pp. 627-644 ◽  
Author(s):  
Mohammad Hassan TAYEBI ◽  
Majid HASHEMI TANGESTANI ◽  
Robert Keller VINCENT
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Spectral Ratio ◽  
Mineral Mapping ◽  
Aster Data ◽  
Ratio Imaging ◽  
Som Neural Network

Research on the Fault Diagnosis Technology of Diesel Engine Based on the Instantaneous Speed

2009 ◽  
Vol 413-414 ◽  
pp. 547-552 ◽  
Author(s):  
Yi Hu ◽  
Rui Ping Zhou ◽  
Jian Guo Yang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Vibration Amplitude ◽  
Good Effect ◽  
Cylinder Pressure ◽  
Angular Vibration ◽  
Diagnosis Method ◽  
Som Neural Network ◽  
Instantaneous Speed

The instantaneous speed signals of diesel contain lots of information about machine states, which is useful for fault diagnosis of diesel engine. Mixed fault diagnosis method of diesel engine based on the instantaneous speed has been proposed, which combines with the lower order angular vibration amplitude and SOM neural network to diagnose the cylinder pressure fault, then extracts three feature parameters of instantaneous speed to locate the fault cylinder. The method can detect the cylinder pressure fault accurately in diesel engine and locate the fault cylinder. The experimental confirmation shows that it has good effect on fault diagnosis of diesel engine.

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