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Error-minimizing dead zone for basis function networks.

¹Inst. fur Allgemeine Elektrotechnik Automobilelektronik, Tech. Univ. of Vienna.

IEEE Transactions on Neural Networks

|January 1, 1996

Summary

This summary is machine-generated.

Incorporating dead zones into basis function networks prevents overtraining and ensures convergence for normalized least mean square (LMS) algorithms. A novel error-minimizing dead zone offers superior performance compared to traditional methods.

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Area of Science:

Machine Learning
Signal Processing
Adaptive Systems

Background:

Basis function networks are susceptible to overtraining.
Convergence of normalized least mean square (LMS) algorithms can be challenging.
Dead zones in error signals are known to improve network stability.

Purpose of the Study:

To introduce a new dead zone strategy for basis function networks.
To guarantee the convergence of normalized least mean square (LMS) algorithms.
To present an error-minimizing dead zone that optimizes performance.

Main Methods:

Developing a general convergence proof for LMS algorithms with dead zones.
Deriving the error-minimizing dead zone from convergence conditions.
Comparing the performance of the new dead zone with classical dead zones.

Main Results:

The proposed error-minimizing dead zone provides the least a posteriori error among convergence-assuring dead zones.
The incorporation of dead zones effectively prevents overtraining in basis function networks.
The new dead zone demonstrates superior performance compared to classical approaches.

Conclusions:

The novel error-minimizing dead zone enhances the stability and performance of LMS algorithms in basis function networks.
This approach offers a robust solution for preventing overtraining and ensuring reliable convergence.
The findings provide a valuable advancement in adaptive signal processing and machine learning.