MATLAB Code Implementation for SOM Algorithm
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This article presents the source code implementation for the Self-Organizing Map (SOM) algorithm, an unsupervised neural network learning method that projects high-dimensional data onto lower-dimensional spaces. The algorithm was originally proposed by Teuvo Kohonen from Helsinki University of Technology in 1982. The MATLAB implementation typically involves key functions such as Newsom for network creation, Trains for competitive learning, and Sim for mapping new data. While SOM algorithm implementations in MATLAB are common in academic research, industrial applications often prefer implementations in languages like Python or C++ for better performance and integration capabilities. Therefore, when deploying this algorithm in industrial production environments, appropriate code conversion and adaptation are necessary, including optimization of neighborhood functions, learning rate adjustments, and potential parallelization for large-scale datasets.
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