Abstract
Kohonen self-organization algorithm, known as “topologic maps algorithm”, has been largely used in many applications for classification. However, few theoretical studies have been proposed to improve and optimize the learning process of classification and clustering for dynamic and scalable systems taking into account the evolution of multi-parameter objects. Our objective in this paper is to provide a new approach to improve the accuracy and quality of the classification method based on the basic advantages of the Kohonen self-organization algorithm and on new network functions to pre-eliminate the auto-detected of drawbacks and redundancy.
Highlights
The self-organizing map of Kohonen (SOM), is a model of artificial neural networks (ANN) widely used in different domains, in the classification and clustering of multi-parameter objects
The length of the vectors used in this example, has been adjusted to have the possibility of presenting input data into a two-dimensional space, which facilitates the understanding of the problems and improves results interpretation
Part A of the figure shows that the input data treatment and optimization block (IDOB) has well filtered and reduced the initial data and the new dimension of the matrix formed by the block is (4x1)
Summary
The self-organizing map of Kohonen (SOM), is a model of artificial neural networks (ANN) widely used in different domains, in the classification and clustering of multi-parameter objects. These intelligent systems are characterized by special abilities such as learning, adaptation and the possibility of visualization of Multiparameter objects with a reduced space. Our objective is to reveal the ambiguities and obstacles that may limit the application of this paradigm in the different domains of human activities, and find possible solutions to eliminate them. The tests performed and presented show that the proposed model of the map is more advantageous to be used as a means for the creation and the application of intelligent systems in the various fields of human activities
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