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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

Flexible structure multiple modeling using irregular self-organizing maps neural network.

Alireza Fatehi¹, Kenichi Abe

¹Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran. fatehi@kntu.ac.ir

International Journal of Neural Systems

|July 3, 2008

Summary

This study introduces the Multiple Modeling by Irregular Self-Organizing Map (MMISOM) for adaptive system identification. The improved MMISOM offers greater flexibility in modeling complex, concave plant model spaces.

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Area of Science:

Artificial Intelligence
Machine Learning
Adaptive Control Systems

Background:

Traditional system identification methods struggle with complex, dynamic systems.
The authors previously developed the Multiple Model Self-Organizing Map (MMSOM) for system identification.

Purpose of the Study:

To enhance the MMSOM method for improved adaptive system identification.
To introduce the Multiple Modeling by Irregular Self-Organizing Map (MMISOM) technique.
To evaluate the flexibility and performance of MMISOM in modeling concave linear model spaces.

Main Methods:

The MMISOM utilizes an Irregular Self-Organizing Map (ISOM) as its core component.
Neural networks are employed, taking adaptive instantaneous model parameters as input.
The ISOM is structured as a minimum spanning tree graph connecting nodes.
The method adaptively selects the best local model matching the plant output at each instant.

Main Results:

MMISOM demonstrates enhanced flexibility in covering concave linear model spaces.
The method allows for the addition of new models dynamically.
The neural network effectively learns and estimates parameters for local models.

Conclusions:

The MMISOM represents a significant improvement over previous methods for adaptive system identification.
MMISOM provides a more robust and flexible approach to modeling complex plant dynamics.
This technique is particularly advantageous for systems with concave model spaces.