A New Method of Color Map Segmentation Based on the Self-organizing Neural Network

2009 ◽  
pp. 417-423 ◽  
Author(s):  
Zhenqing Xue ◽  
Chunpu Jia
Keyword(s):  
Neural Network ◽  
The Self ◽  
New Method ◽  
Map Segmentation ◽  
Color Map ◽  
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.

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

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.

Clustering Urban Multifunctional Landscapes Using the Self-Organizing Feature Map Neural Network Model

2014 ◽  
Vol 140 (2) ◽  
pp. 05014001 ◽  
Author(s):  
Yang Gao ◽  
Zhe Feng ◽  
Yang Wang ◽  
Jin-Long Liu ◽  
Shuang-Cheng Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
The Self ◽  
Feature Map ◽  
Multifunctional Landscapes ◽  
Self Organizing

scholarly journals Score Prediction of Sports Events Based on Parallel Self-Organizing Nonlinear Neural Network

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Junyao Ling
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Phase Space ◽  
Development Trend ◽  
The Self ◽  
Minimum Error ◽  
Main Research ◽  
True Value ◽  
Self Organizing Networks ◽  
Self Organizing

This paper introduces the basic concepts and main characteristics of parallel self-organizing networks and analyzes and predicts parallel self-organizing networks through neural networks and their hybrid models. First, we train and describe the law and development trend of the parallel self-organizing network through historical data of the parallel self-organizing network and then use the discovered law to predict the performance of the new data and compare it with its true value. Second, this paper takes the prediction and application of chaotic parallel self-organizing networks as the main research line and neural networks as the main research method. Based on the summary and analysis of traditional neural networks, it jumps out of inertial thinking and first proposes phase space. Reconstruction parameters and neural network structure parameters are unified and optimized, and then, the idea of dividing the phase space into multiple subspaces is proposed. The multi-neural network method is adopted to track and predict the local trajectory of the chaotic attractor in the subspace with high precision to improve overall forecasting performance. During the experiment, short-term and longer-term prediction experiments were performed on the chaotic parallel self-organizing network. The results show that not only the accuracy of the simulation results is greatly improved but also the prediction performance of the real data observed in reality is also greatly improved. When predicting the parallel self-organizing network, the minimum error of the self-organizing difference model is 0.3691, and the minimum error of the self-organizing autoregressive neural network is 0.008, and neural network minimum error is 0.0081. In the parallel self-organizing network prediction of sports event scores, the errors of the above models are 0.0174, 0.0081, 0.0135, and 0.0381, respectively.

scholarly journals A HYBRID KANSEI ENGINEERING SYSTEM USING THE SELF-ORGANIZING MAP NEURAL NETWORK

2016 ◽  
Vol 2 (1) ◽  
pp. 23-38
Author(s):  
C.S. Teh ◽  
C.P. Lim
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Product Design ◽  
The Self ◽  
Design Parameters ◽  
Decision Making Process ◽  
Engineering System ◽  
Self Organizing Map ◽  
Kansei Engineering ◽  
Self Organizing

Kansei Engineering (KE), a technology founded in Japan initially for product design, translates human feelings into design parameters. Although various intelligent approaches to objectively model human functions and the relationships with the product design decisions have been introduced in KE systems, many of the approaches are not able to incorporate human subjective feelings and preferences into the decision-making process. This paper proposes a new hybrid KE system that attempts to make the machine-based decision-making process closely resembles the real-world practice. The proposed approach assimilates human perceptive and associative abilities into the decision-making process of the computer. A number of techniques based on the Self-Organizing Map (SOM) neural network are employed in the backward KE system to reveal the underlying data structures that are involved in the decision-making process. A case study on interior design is presented to evaluate the efficacy of the proposed approach. The results obtained demonstrate the effectiveness of the proposed approach in developing an intelligent KE system which is able to combine human feelings and preferences into its decision making process.

scholarly journals Haptic Perception with Self-Organizing ANNs and an Anthropomorphic Robot Hand

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Magnus Johnsson ◽  
Christian Balkenius
Keyword(s):  
Neural Network ◽  
Haptic Perception ◽  
Cell Structure ◽  
The Self ◽  
Robot Hand ◽  
Self Organizing Map ◽  
Training Set ◽  
Input Space ◽  
Growing Cell ◽  
Self Organizing

We have implemented and compared four biologically motivated self-organizing haptic systems based on proprioception. All systems employ a 12-d.o.f. anthropomorphic robot hand, the LUCS Haptic Hand 3. The four systems differ in the kind of self-organizing neural network used for clustering. For the mapping of the explored objects, one system uses a Self-Organizing Map (SOM), one uses a Growing Cell Structure (GCS), one uses a Growing Cell Structure with Deletion of Neurons (GCS-DN), and one uses a Growing Grid (GG). The systems were trained and tested with 10 different objects of different sizes from two different shape categories. The generalization abilities of the systems were tested with 6 new objects. The systems showed good performance with the objects from the training set as well as in the generalization experiments. Thus the systems could discriminate individual objects, and they clustered the activities into small cylinders, large cylinders, small blocks, and large blocks. Moreover, the self-organizing ANNs were also organized according to size. The GCS-DN system also evolved disconnected networks representing the different clusters in the input space (small cylinders, large cylinders, small blocks, large blocks), and the generalization samples activated neurons in a proper subnetwork in all but one case.

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