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Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

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A growing and pruning method for radial basis function networks.

M Bortman¹, M Aladjem

¹Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 84105 Beer-Sheva, Israel. aladjem@ee.bgu.ac.il

IEEE Transactions on Neural Networks

|May 19, 2009

Summary

This summary is machine-generated.

This study modifies the Generalized Growing and Pruning (GGAP) algorithm for radial basis function (RBF) neural networks. The enhanced algorithm improves prediction accuracy and reduces network complexity, making it suitable for high-dimensional data.

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Area of Science:

Machine Learning
Artificial Intelligence
Computational Neuroscience

Background:

Radial Basis Function (RBF) neural networks are powerful tools for complex pattern recognition.
Resource-allocating network (RAN) algorithms, like Generalized Growing and Pruning (GGAP), dynamically adjust network complexity during training.
The original GGAP algorithm's unit significance calculation requires computationally intensive d-fold numerical integration for arbitrary input data probability density functions (p(x)).

Purpose of the Study:

To modify the GGAP algorithm for enhanced efficiency and applicability.
To enable GGAP to handle complex and high-dimensional input data probability density functions.
To improve the prediction accuracy and reduce the complexity of trained RBF neural networks.

Main Methods:

Approximation of the GGAP unit significance formula using a Gaussian Mixture Model (GMM) for p(x).
Derivation of an analytical solution for the approximated unit significance.
Extensive experimental validation comparing the modified GGAP with the original algorithm.

Main Results:

The modified GGAP algorithm successfully handles complex and high-dimensional p(x), overcoming limitations of the original GGAP.
Experimental results demonstrate superior performance of the modified algorithm over the original GGAP.
The modified algorithm achieved both a lower prediction error and a reduced complexity in the trained RBF neural networks.

Conclusions:

The GMM-based approximation and analytical solution provide an efficient and effective modification of the GGAP algorithm.
The enhanced GGAP algorithm offers improved performance and broader applicability for RBF neural network training, especially with complex, high-dimensional data.
This work significantly advances the practical utility of GGAP in machine learning applications.