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Possibility-based fuzzy neural networks and their application to image processing.

L Chen¹, D H Cooley, J Zhang

¹Dept. of Comput. Sci., Utah State Univ., Logan, UT.

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

|February 7, 2008

Summary

This study introduces a novel two-stage fuzzy neural network for complex data classification. This hybrid model integrates fuzzy logic and neural networks, offering simpler configurations and enhanced input flexibility for pattern recognition tasks.

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Area of Science:

Artificial Intelligence
Machine Learning
Computational Intelligence

Background:

Standard neural networks face limitations in classifying complex feature sets and handling diverse input types.
Fuzzy logic offers a framework for representing and processing uncertainty and vagueness in data.

Purpose of the Study:

To establish the theoretical foundations for a new class of composite fuzzy neural networks.
To demonstrate the efficacy of these networks in handling complex classification problems.

Main Methods:

A two-stage architecture combining a fuzzy network stage and a neural network stage.
The fuzzy stage includes a parameter computing network (PCN) with fuzzy weights and a converting layer.
The neural stage utilizes a standard backpropagation algorithm.

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Main Results:

The proposed fuzzy neural network can classify complex feature set vectors using a simpler configuration than standard neural networks.
The network accepts both scalar values and possibility functions as input.
Successful applications demonstrated in satellite image processing and seismic lithology pattern recognition.

Conclusions:

Fuzzy neural networks provide a powerful and flexible approach for complex pattern recognition.
This hybrid architecture offers advantages in terms of input handling and model simplicity.