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Related Experiment Video

Updated: Jul 7, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Kernel orthonormalization in radial basis function neural networks.

W Kaminski¹, P Strumillo

¹Fac. of Process. and Environ. Eng., Tech. Univ. Lodz.

IEEE Transactions on Neural Networks

|January 1, 1997

Summary

This summary is machine-generated.

This study introduces an efficient method for training radial basis function (RBF) neural networks by orthonormalizing RBF kernels. This significantly speeds up computations and is suitable for hardware implementation.

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Last Updated: Jul 7, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Area of Science:

Computational intelligence
Machine learning
Artificial neural networks

Background:

Radial basis function (RBF) neural networks are widely used for various tasks.
Training RBF networks can be computationally intensive, especially with incremental node addition.

Purpose of the Study:

To optimize the computational processes involved in training RBF neural networks.
To present a novel method for calculating network weights that enhances efficiency and hardware compatibility.

Main Methods:

Transformation of RBF kernels into an orthonormal set using Gram-Schmidt orthogonalization.
Incremental addition of kernel hidden nodes for network performance improvement.
Decomposition of the computational task into parallel subtasks.

Main Results:

Significant reduction in computing time for RBF network training.
The proposed method offers low storage requirements, making it suitable for hardware implementation.
Demonstrated validity on data classification and function approximation problems.

Conclusions:

The orthonormalization method provides a computationally efficient approach to RBF network training.
The technique allows for restoration of the original network structure post-computation.
The parallelizable nature and low storage needs make it attractive for practical applications.