Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Self-Organizing Maps
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
RGFGA: An Efficient Representation and Crossover for Grouping Genetic Algorithms
Evolutionary Computation
Large-scale data exploration with the hierarchically growing hyperbolic SOM
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
TreeSOM: cluster analysis in the self-organizing map
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
A recommender system using GA K-means clustering in an online shopping market
Expert Systems with Applications: An International Journal
An integration of bidding-oriented product conceptualization and supply chain formation
Computers in Industry
Application of ant K-means on clustering analysis
Computers & Mathematics with Applications
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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This study attempts to employ growing self-organizing map (GSOM) algorithm and continuous genetic algorithm (CGA)-based SOM (CGASOM) to improve the performance of SOM neural network (SOMnn). The proposed GSOM+CGASOM approach for SOMnn is consisted of two stages. The first stage determines the SOMnn topology using GSOM algorithm while the weights are fine-tuned by using CGASOM algorithm in the second stage. The proposed CGASOM algorithm is compared with other two clustering algorithms using four benchmark data sets, Iris, Wine, Vowel, and Glass. The simulation results indicate that CGASOM algorithm is able to find the better solution. Additionally, the proposed approach has been also employed to grade Lithium-ion cells and characterize the quality inspection rules. The results can assist the battery manufacturers to improve the quality and decrease the costs of battery design and manufacturing.