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Propagation of Waves

Propagation of Waves

When a wave propagates from one medium to another, part of it may get reflected in the first medium, and part of it may get transmitted to the second medium. In such a case, the interface of the two mediums can be considered as a boundary that is neither fixed nor free.
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Effective Value of a Periodic Waveform

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Related Experiment Video

Updated: Jul 7, 2026

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

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Published on: May 10, 2017

Wavelet networks.

Q Zhang¹, A Benveniste

¹Linkopings Tekniska Hogskola.

IEEE Transactions on Neural Networks

|January 1, 1992

Summary

This summary is machine-generated.

This study introduces wavelet networks, using "wavelons" instead of neurons, as a novel approach for approximating nonlinear functions. A backpropagation-like algorithm trains these networks, showing promise as an alternative to traditional feedforward neural networks.

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Area of Science:

Artificial Intelligence
Machine Learning
Signal Processing

Background:

Feedforward neural networks are widely used for approximating nonlinear functions.
Traditional neural networks can be computationally intensive and complex to train.
Wavelet transform theory offers a powerful framework for analyzing signals and functions.

Purpose of the Study:

To propose wavelet networks as an alternative to feedforward neural networks.
To introduce a novel computing unit called a "wavelon" for function approximation.
To develop a training algorithm for wavelet networks.

Main Methods:

The proposed wavelet network replaces standard neurons with "wavelons."
Each "wavelon" combines an affine transform and a multidimensional wavelet.
A backpropagation-type algorithm is developed for training the network by identifying affine transforms and synaptic weights.

Main Results:

Experimental results demonstrate the effectiveness of the proposed wavelet network.
The network successfully approximates arbitrary nonlinear functions.
The training algorithm effectively identifies network parameters from noisy data.

Conclusions:

Wavelet networks offer a viable alternative to feedforward neural networks for nonlinear function approximation.
"Wavelons" provide a powerful building block for these networks.
The developed backpropagation-type algorithm enables efficient training of wavelet networks.