scholarly journals An Approach for Fuzzy Modeling based on Self-Organizing Feature Maps Neural Network

2014 ◽  
Vol 8 (3) ◽  
pp. 1207-1215
Author(s):  
Ching-Yi Chen ◽  
Jen-Shiun Chiang ◽  
Kuang-Yuan Chen ◽  
Ta-Kang Liu ◽  
Ching-Chang Wong
Keyword(s):  
Neural Network ◽  
Fuzzy Modeling ◽  
Feature Maps ◽  
Self Organizing

scholarly journals Classification of chosen orchard pests using the self-organizing feature maps neural network

2012 ◽  
Vol 7 (47) ◽  
pp. 6357-6362 ◽  
Author(s):  
Pilarski Krzysztof ◽  
Boniecki Piotr ◽  
Slosarz Piotr ◽  
Dach Jacek ◽  
Boniecka Piekarska Hanna ◽  
...  
Keyword(s):  
Neural Network ◽  
The Self ◽  
Feature Maps ◽  
Orchard Pests ◽  
Self Organizing

Intelligent Diagnosis Method of MRI Brain Image Using Parallel Self-Organizing Feature Maps Neural Network

2021 ◽  
Vol 11 (2) ◽  
pp. 487-496
Author(s):  
Li Liu ◽  
Chi Hua ◽  
Zixuan Cheng ◽  
Yunfeng Ji
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Medical Imaging ◽  
Automatic Segmentation ◽  
Learning Ability ◽  
Mr Images ◽  
Feature Maps ◽  
Intelligent Diagnosis ◽  
Brain Mr Images ◽  
Self Organizing

Advances in medical imaging skills have promoted the influence of medical imaging in neuroscience. Having advanced medical imaging technology is essential for the medical industry. Magnetic resonance imaging (MRI) plays a central role in medical imaging. It plays a key role in the treatment of various human diseases. Doctors analyze brain size, shape, and location in brain MR images to assess brain disease and develop a medical plan. The manual division of brain tissue by experts is heavy and subjective. Therefore, the study of automatic segmentation of brain MR images has practical significance. Because the characteristics of brain MRI images are low contrast and high noise, which seriously affects the accuracy of image segmentation, the current image segmentation methods have some limitations in this application. In this paper, multiple self-organizing feature maps neural network (SOM-NN) are utilized to construct a parallel self-organizing feature maps neural network (PSOM-NN), which converts the segmentation problem of brain images into the classification problem of PSOMNN. The experiments show that SOM has strong self-learning ability in learning and training, and the parallel ability of PSOM-NN model greatly reduces the segmentation time, improves the real-time performance of the model, and helps to realize fully automatic or semi-automatic segmentation of the lesion area. PSOM can promote the improvement of segmentation accuracy and facilitate intelligent diagnosis.

Modeling student knowledge with self-organizing feature maps.

1993 ◽  
Author(s):  
Steven A. Harp ◽  
Tariq Samad ◽  
Michael Villano
Keyword(s):  
Feature Maps ◽  
Student Knowledge ◽  
Self Organizing

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.

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