FAULT IMMUNIZATION CONCEPT FOR SELF-ORGANIZING MAPPING NEURAL NETWORKS

2001 ◽  
Vol 09 (06) ◽  
pp. 781-790 ◽  
Author(s):  
R. TALUMASSAWATDI ◽  
C. LURSINSAP
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Optimization Problem ◽  
Transmission Reliability ◽  
Real Value ◽  
Som Neural Network ◽  
Compression Area ◽  
Winner Take All ◽  
Take All ◽  
Self Organizing

Self-Organizing Mapping (SOM) neural network has been widely used in pattern classification, vector quantization, and image compression. We consider the problem of strengthening the reliability of a SOM neural network by the technique of fault immunization of the synaptic links of each neuron which is similar to the concept of biological immunization. Instead of assuming the stuck-at-0 and stuck-at-1 as in those studies, we consider a general case of stuck-at-a, where a is a real value. The only assumption that we consider is only one neuron can be faulty at any time. No restriction on the number of faulty links of the neuron. Let wi,j be the weight of synaptic link j of neuron i obtained after the winner-take-all classification. Weight wi,j is immunized by adding a constant ∊i,j, either positive or negative, to wi,j. A neuron reaches its maximum fault immunization if the value of wi,j + ∊i,j can be either increased or decreased as much as possible without creating any misclassification. Thus, the fault immunization problem is formulated as an optimization problem on finding the value of each ∊i,j. A technique to find the value of wi,j + ∊i,j is proposed and its application to enhance the transmission reliability in image compression area is introduced.

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.

Improved winner-take-all neural network

1992 ◽  
Vol 28 (7) ◽  
pp. 662 ◽  
Author(s):  
J.-C. Yen ◽  
S. Chang
Keyword(s):  
Neural Network ◽  
Winner Take All ◽  
Take All

Automatic Detection of Arrow Annotation Overlays in Biomedical Images

2011 ◽  
Vol 6 (4) ◽  
pp. 23-41 ◽  
Author(s):  
Beibei Cheng ◽  
R. Joe Stanley ◽  
Soumya De ◽  
Sameer Antani ◽  
George R. Thoma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Computational Intelligence ◽  
Medical Images ◽  
Characteristic Curve ◽  
Data Set ◽  
Negative Correlation Learning ◽  
Artificial Neural ◽  
Winner Take All ◽  
Take All

Images in biomedical articles are often referenced for clinical decision support, educational purposes, and medical research. Authors-marked annotations such as text labels and symbols overlaid on these images are used to highlight regions of interest which are then referenced in the caption text or figure citations in the articles. Detecting and recognizing such symbols is valuable for improving biomedical information retrieval. In this research, image processing and computational intelligence methods are integrated for object segmentation and discrimination and applied to the problem of detecting arrows on these images. Evolving Artificial Neural Networks (EANNs) and Evolving Artificial Neural Network Ensembles (EANNEs) computational intelligence-based algorithms are developed to recognize overlays, specifically arrows, in medical images. For these discrimination techniques, EANNs use particle swarm optimization and genetic algorithm for artificial neural network (ANN) training, and EANNEs utilize the number of ANNs generated in an ensemble and negative correlation learning for neural network training based on averaging and Linear Vector Quantization (LVQ) winner-take-all approaches. Experiments performed on medical images from the imageCLEFmed’08 data set, yielded area under the receiver operating characteristic curve and precision/recall results as high as 0.988 and 0.928/0.973, respectively, using the EANNEs method with the winner-take-all approach.

scholarly journals Stereo Imaging Using Hardwired Self-Organizing Object Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5833
Author(s):  
Ching-Han Chen ◽  
Guan-Wei Lan ◽  
Ching-Yi Chen ◽  
Yen-Hsiang Huang
Keyword(s):  
Neural Network ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Imaging System ◽  
Object Segmentation ◽  
Hierarchical Architecture ◽  
Absolute Difference ◽  
Stereo Imaging ◽  
Som Neural Network ◽  
Self Organizing

Stereo vision utilizes two cameras to acquire two respective images, and then determines the depth map by calculating the disparity between two images. In general, object segmentation and stereo matching are some of the important technologies that are often used in establishing stereo vision systems. In this study, we implement a highly efficient self-organizing map (SOM) neural network hardware accelerator as unsupervised color segmentation for real-time stereo imaging. The stereo imaging system is established by pipelined, hierarchical architecture, which includes an SOM neural network module, a connected component labeling module, and a sum-of-absolute-difference-based stereo matching module. The experiment is conducted on a hardware resources-constrained embedded system. The performance of stereo imaging system is able to achieve 13.8 frames per second of 640 × 480 resolution color images.

CENTROID NEURAL NETWORK WITH WEIGHTED FEATURES

2009 ◽  
Vol 18 (08) ◽  
pp. 1353-1367 ◽  
Author(s):  
DONG-CHUL PARK
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Objective Function ◽  
Lagrange Multiplier ◽  
Synthetic Data ◽  
Feature Weighting ◽  
The Self ◽  
Self Organizing Map ◽  
Data Set ◽  
Self Organizing

A Centroid Neural Network with Weighted Features (CNN-WF) is proposed and presented in this paper. The proposed CNN-WF is based on a Centroid Neural Network (CNN), an effective clustering tool that has been successfully applied to various problems. In order to evaluate the importance of each feature in a set of data, a feature weighting concept is introduced to the Centroid Neural Network in the proposed algorithm. The weight update equations for CNN-WF are derived by applying the Lagrange multiplier procedure to the objective function constructed for CNN-WF in this paper. The use of weighted features makes it possible to assess the importance of each feature and to reject features that can be considered as noise in data. Experiments on a synthetic data set and a typical image compression problem show that the proposed CNN-WF can assess the importance of each feature and the proposed CNN-WF outperforms conventional algorithms including the Self-Organizing Map (SOM) and CNN in terms of clustering accuracy.

A general mean-based iterative winner-take-all neural network

1995 ◽  
Vol 6 (1) ◽  
pp. 14-24 ◽  
Author(s):  
Jar-Ferr Yang ◽  
Chi-Ming Chen ◽  
Wen-Chung Wang ◽  
Jau-Yien Lee
Keyword(s):  
Neural Network ◽  
Winner Take All ◽  
Take All
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