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Functional equivalence between radial basis function networks and fuzzy inference systems.

J R Jang¹, C T Sun

¹Dept. of Electr. and Comput. Sci., California Univ., Berkeley, CA.

IEEE Transactions on Neural Networks

|January 1, 1993

Summary

This summary is machine-generated.

Radial basis function networks (RBFNs) and fuzzy inference systems exhibit equivalent functional behavior. This finding allows knowledge transfer between these distinct artificial intelligence models.

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Area of Science:

Artificial Intelligence
Machine Learning
Computational Intelligence

Background:

Radial basis function networks (RBFNs) and fuzzy inference systems (FIS) are prominent computational models.
These models originate from different theoretical frameworks and application domains.
Despite differing origins, their underlying functional capabilities warrant investigation.

Purpose of the Study:

To establish the functional equivalence between RBFNs and FIS.
To explore the implications of this equivalence for knowledge transfer and model development.
To highlight the convergence of distinct artificial intelligence paradigms.

Main Methods:

Theoretical analysis of the functional mapping capabilities of RBFNs.
Theoretical analysis of the functional mapping capabilities of FIS.
Comparative study to demonstrate the equivalence under specified conditions.

Main Results:

Demonstration of functional equivalence between RBFN and FIS architectures.
Identification of conditions under which the equivalence holds.
Implication that learning rules and representational power are transferable between models.

Conclusions:

RBFNs and FIS are functionally interchangeable under certain constraints.
This equivalence facilitates cross-pollination of research findings and techniques.
The study underscores a fundamental unity in diverse intelligent system designs.