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

This documentation presents MATLAB source code for Self-Organizing Map (SOM) neural networks along with corresponding experimental data. The implementation includes key algorithmic components such as the competitive learning mechanism where neurons compete to respond to input patterns, and the neighborhood function that determines how neighboring neurons update their weights. The discussion can be extended to explore the network's working principles through code analysis, including the weight initialization methods and the iterative training process where weights are adjusted using the formula: w_new = w_old + η(t) * h(t) * (x - w_old). Here, η represents the learning rate that typically decreases over time, while h denotes the neighborhood function. Practical applications in data mining and image processing can be demonstrated through the provided experimental datasets, showing how SOMs perform dimensionality reduction and clustering. The experimental data analysis includes statistical methods such as variance calculation (σ² = Σ(xᵢ - μ)²/n) and standard deviation measurements to evaluate clustering quality and neuron activation patterns. Further examination of algorithm details covers weight update mechanisms, learning rate scheduling strategies (linear/exponential decay), and neighborhood radius adjustment during training phases. The experimental data facilitates understanding of quantization error and topographic error metrics, essential for evaluating SOM performance in pattern recognition tasks. This comprehensive resource provides a foundation for expanding research directions in neural network optimization, feature mapping techniques, and unsupervised learning applications.