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Related Experiment Video

Updated: Jul 7, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Identification and control of dynamical systems using the self-organizing map.

G A Barreto¹, A R Araujo

¹Dept. of Teleinformatics Eng., Fed. Univ. of Ceara, Fortaleza-CE, Brazil.

IEEE Transactions on Neural Networks

|February 2, 2008

Summary

We introduce a novel Vector-Quantized Temporal Associative Memory (VQTAM) using Self-Organizing Maps (SOMs) for dynamical system modeling. VQTAM offers accurate and robust performance, comparable to Multilayer Perceptrons and superior to Radial Basis Functions.

Related Experiment Videos

Last Updated: Jul 7, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Area of Science:

Artificial Intelligence
Machine Learning
Dynamical Systems

Background:

Dynamical system identification and control commonly utilize Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural models.
Limitations exist in current models regarding training sensitivity and error reduction.

Purpose of the Study:

Introduce a novel Vector-Quantized Temporal Associative Memory (VQTAM) technique.
Utilize Kohonen's Self-Organizing Map (SOM) as an alternative neural model for dynamical systems.
Evaluate VQTAM's performance in complex identification and control tasks.

Main Methods:

Developed the VQTAM technique employing SOMs.
Demonstrated VQTAM as a self-supervised gradient-based error reduction method.
Evaluated performance on time series prediction, SISO/MIMO system identification, and nonlinear predictive control.

Main Results:

VQTAM achieves estimation error reduction during SOM training.
Simulation results show SOM performance is as accurate as MLP and superior to RBF networks.
SOM demonstrates reduced sensitivity to weight initialization compared to MLP.

Conclusions:

VQTAM is a viable and effective alternative for dynamical system identification and control.
The properties of VQTAM align with established system identification methods.
Further research directions for VQTAM are proposed.