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Multiplication-free radial basis function network.

M Heiss¹, S Kampl

¹Inst. fur Allgemeine Elekrotechnik-Automobilelektronik, Vienna Univ. of Technol., Vienna.

IEEE Transactions on Neural Networks

|January 1, 1996

Summary

This summary is machine-generated.

A novel radial basis function network reduces computational load for adaptive function approximation. This new method avoids multiplication for efficient online implementation on microcomputers.

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Area of Science:

Computational intelligence
Machine learning
Artificial neural networks

Background:

Adaptive function approximation is crucial for many computational tasks.
Existing methods often require significant computational resources, limiting real-time applications.
Efficient parameter adaptation is key for online learning systems.

Purpose of the Study:

To propose a new radial basis function network that is nonlinear in its parameters.
To significantly reduce computational effort for serial processors.
To enable efficient online implementation on microcomputers.

Main Methods:

Utilizing a grid-based Gaussian basis function network for function approximation.
Avoiding multiplication in both function model evaluation and parameter adaptation.
Developing and proving convergence for a gradient descent-based nonlinear learning algorithm.

Main Results:

The proposed network achieves adaptive function approximation with reduced computational complexity.
The local support of Gaussian functions facilitates parametric local representation.
The method is well-suited for online implementation on microcomputers.

Conclusions:

The new radial basis function network offers a computationally efficient approach to adaptive function approximation.
The avoidance of multiplication significantly lowers the computational burden.
The algorithm's proven convergence ensures reliable performance in online learning scenarios.