A Study of Self- Organizing Maps(SOM) Neural Network Using Matlab

Abstract

Kohonen's Self-Organizing Map (SOM) is one of the most popular artificial neural network algorithms.

Bing unsupervised neural network , Self Organizing Maps(SOM) have applications in different fields such as speech recognition, image processing and so on . This project includes a study of Self Organizing Maps neural network  using  MATLAB The structure, characteristics, implementation, applications and testing of this neural network for styles of one dimension and two dimensions have been considered It includes finding out the functions used for topology, and then finding out the distance function for this network throughout illustrative examples. Neural network implementation consists of three stages: Initialization (creation), Training, and Simulating. Explanation of the neighborhood concept is done. MATLAB software is used to perform how creating, training and simulating of a Self Organizing Map. Creation process consists of choosing a network parameters, plotting the results, then illustrating and identifying all functions used to create a Self Organizing Map. Training consists of weight initialization and weight vector creation. Simulating means testing the neural network using the initialization parameters and training vector created in the past two stages. The Study includes also four tests: The first test is used manual calculation procedure of the network mathematically.  The other three tests are procedures for different applications using MATLAB language. It becomes evident from the graphs of the results that it’s essential to have the weight vectors for the coordination field greater than the density of input vectors in the case of employing the network to seclude the training styles in output cells.

Keywords: Neural Network, Simulating, Self- Organizing Maps, Competitive layer, training

DOI: 10.7176/RHSS/10-6-01

Publication date:March 31^st 2020