MATLAB Source Code for Self-Organizing Map Neural Network with Experimental Data

Self-Organizing Map (SOM) is an unsupervised learning-based neural network model primarily used for high-dimensional data dimensionality reduction and cluster visualization. In MATLAB, it can be implemented using either the built-in Neural Network Toolbox or custom code.

Implementation Approach The MATLAB implementation of Self-Organizing Map neural networks typically involves the following steps:

Data Preprocessing: Input data requires normalization to enable effective feature distribution learning. Common methods include standardization or Min-Max scaling, implemented using functions like `zscore` or custom normalization scripts.

Network Initialization: Determine neuron arrangement (e.g., 2D grid) and initialize weights. MATLAB's `selforgmap` function automates this process by creating a SOM network with specified dimensions and training parameters.

Training Process: Employing competitive learning mechanism, weights are iteratively adjusted through functions like `train` or `trains` to make neurons gradually approximate data distribution in input space. The training consists of two phases: coarse-tuning (rapid organization) and fine-tuning (precision adjustment), controlled by learning rate and neighborhood radius parameters.

Visualization Analysis: MATLAB provides multiple tools including U-matrix, sample hit maps, and neighbor weight plots through functions like `plotsomhits` and `plotsomnd` for analyzing SOM training results and understanding cluster structures.

Experimental Data Applications Typical experimental datasets may include financial time series, biometric data, or image feature vectors. SOM effectively discovers latent patterns in data, suitable for applications like market segmentation and gene expression analysis using datasets from domains such as finance or bioinformatics.

Extension Approaches Combine with other machine learning methods (e.g., K-means clustering) to optimize clustering results through post-processing algorithms. Utilize GPU acceleration with `gpuArray` functions for large dataset processing. Adjust learning rates and neighborhood radius parameters programmatically to improve model convergence speed and accuracy through parameter tuning scripts.