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Neuro-fuzzy rule generation: survey in soft computing framework.

S Mitra¹, Y Hayashi

¹Machine Intelligence Unit, Indian Statistical Institute, Calcutta 700 035, India. sushmita@isical.ac.in

IEEE Transactions on Neural Networks

|February 6, 2008

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This survey categorizes neuro-fuzzy rule generation algorithms, combining artificial neural networks and fuzzy logic for human-readable insights. It explores rule extraction, refinement, and applications in fuzzy control and medical diagnosis.

Area of Science:

Artificial Intelligence
Soft Computing
Machine Learning

Background:

Neuro-fuzzy systems integrate artificial neural networks (ANNs) and fuzzy logic for enhanced reasoning.
Rule generation from ANNs offers symbolic knowledge insights, while fuzzy sets handle uncertainty.
Existing literature lacks a unified categorization of neuro-fuzzy rule generation models.

Purpose of the Study:

To provide an exhaustive survey and integrated categorization of neuro-fuzzy rule generation algorithms.
To unify various neuro-fuzzy models within a soft computing framework.
To include rule extraction and refinement in the broader context of rule generation.

Main Methods:

Categorization of neuro-fuzzy models based on their level of synthesis.

Related Experiment Videos

Inclusion of rule generation for fuzzy reasoning and fuzzy control.

Emphasis on integrating other soft computing tools like genetic algorithms and rough sets.

Main Results:

A comprehensive categorization of neuro-fuzzy rule generation models is presented.
The study highlights the synergy between connectionist and fuzzy approaches.
Rule generation from fuzzy knowledge-based networks leads to more refined rules.

Conclusions:

The neuro-fuzzy approach is a key component of soft computing, offering human-comprehensible knowledge extraction.
This work provides a unified framework for understanding diverse neuro-fuzzy rule generation techniques.
Applications in medical diagnosis demonstrate the practical utility of these refined rules.