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TASOM: a new time adaptive self-organizing map.

H Shah-Hosseini¹, R Safabakhsh

¹Dept. of Comput. Eng., Amirkabir Univ. of Technol., Tehran, Iran.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 2, 2008

Summary

The time adaptive self-organizing map (TASOM) network adapts learning rates and neighborhood sizes for improved performance. This novel approach enhances image processing, object tracking, and clustering in dynamic environments.

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Area of Science:

Artificial Intelligence
Machine Learning
Computational Neuroscience

Background:

Self-organizing map (SOM) networks are powerful unsupervised learning tools.
Traditional SOMs have fixed learning parameters, limiting adaptability.
Dynamic environments require adaptive learning mechanisms.

Purpose of the Study:

To introduce the time adaptive self-organizing map (TASOM) network.
To enhance SOM adaptability through dynamic learning rates and neighborhood sizes.
To demonstrate TASOM's effectiveness in diverse applications.

Main Methods:

Modified SOM architecture with neuron-specific adaptive learning rates and neighborhood sizes.
Update rules for winning and neighboring neurons based on input vectors.

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Inclusion of a scaling vector for transformation compensation.

Development of several TASOM-based network versions.

Main Results:

TASOM successfully adapts learning parameters to changing environments.
Proposed TASOM networks show satisfactory performance in bilevel image thresholding.
TASOM demonstrates effectiveness in tracking moving objects and their boundaries.
Adaptive clustering using TASOM yields positive simulation results.

Conclusions:

The TASOM network offers a significant advancement over traditional SOMs.
Adaptive learning parameters enable robust performance in dynamic scenarios.
TASOM provides a versatile framework for image analysis, object tracking, and clustering.