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Blind Procedures

Blind Procedures

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was...

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Blind equalization using a predictive radial basis function neural network.

Nan Xie¹, Henry Leung

¹Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada. xie@ucalgary.ca

IEEE Transactions on Neural Networks

|June 9, 2005

Summary

This summary is machine-generated.

This study introduces a new blind equalization method using radial basis function (RBF) neural networks and improved least squares (ILS) for robust system identification, even with noise. The technique effectively identifies unknown systems and performs well in radar and speech signal applications.

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Area of Science:

Signal Processing
Machine Learning
Neural Networks

Background:

Blind equalization is crucial for recovering signals distorted by unknown channels.
Traditional methods struggle with noisy environments and complex channel effects.
Radial basis function (RBF) neural networks offer potential for adaptive system identification.

Purpose of the Study:

To propose a novel blind equalization approach using RBF neural networks.
To enhance system identification performance in noisy conditions.
To validate the method's effectiveness in practical applications like radar and speech signal processing.

Main Methods:

Utilizing RBF neural networks for predicting the inverse filter output.
Employing an improved least squares (ILS) method for training to mitigate noise-induced bias.
Theoretical analysis of ILS convergence rate and asymptotic mean square error (MSE) of the RBF identification method.
Validation through Monte Carlo simulations.

Main Results:

Minimizing RBF prediction error leads to accurate identification of unknown system coefficients.
The ILS method effectively reduces estimation bias in noisy environments.
Theoretical analysis provides convergence and error bounds for the proposed method.
Demonstrated effectiveness in blind system identification through simulations.

Conclusions:

The proposed RBF neural network-based blind equalization is effective for system identification.
The ILS training enhances robustness against additive measurement noise.
The technique performs satisfactorily in real-world applications, including radar sea clutter equalization and speech deconvolution, even under strong channel effects and noise.