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Routh-Hurwitz Criterion II

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Related Experiment Videos

A fault-tolerant regularizer for RBF networks.

Chi-Sing Leung¹, John Pui-Fai Sum

¹Department of Electronic Engineering, the City Universityof Hong Kong, Kowloon Tong, Hong Kong. eeleungc@cityu.edu.hk

IEEE Transactions on Neural Networks

|March 13, 2008

Summary

This summary is machine-generated.

This study introduces a computationally simple method using Kullback-Leibler divergence to enhance fault tolerance in radial basis function (RBF) networks. The approach improves network resilience and generalization, outperforming conventional methods.

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Area of Science:

Artificial Intelligence
Machine Learning
Neural Networks

Background:

Classical fault tolerance methods for radial basis function (RBF) networks become computationally complex when considering multinode faults.
The large space of potential faulty networks complicates objective functions and learning algorithms.

Purpose of the Study:

To develop a computationally simple and effective method for improving the fault tolerance of RBF networks.
To enhance the generalization ability of RBF networks, even in fault-free scenarios.

Main Methods:

Utilized Kullback-Leibler divergence to define a novel objective function for RBF networks.
Identified a regularizer within the objective function, assuming Gaussian distributed noise in output data.
Developed a corresponding learning algorithm for the proposed objective function.

Main Results:

The proposed objective function and learning algorithm are computationally simple.
The approach demonstrates superior fault-tolerant ability compared to conventional methods like weight-decay regularizers.
Empirical studies confirm improved generalization ability for fault-free RBF networks.

Conclusions:

The Kullback-Leibler divergence-based approach offers a computationally efficient and effective solution for RBF network fault tolerance.
This method enhances both fault resilience and generalization capabilities of RBF networks.