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A Wavelet-Based Neural Network Classifier for Temporal Data

By Brendon J. Woodford and Nikola K. Kasabov

Abstract

This paper outlines the application of Evolving Fuzzy Neural Networks (EFuNN) for the detection of browning in Braebrun apples when in Controlled Atmosphere (CA) storage. Wavelet coefficients are extracted from each image and these features used to train both an EFuNN and a Multi Layer Perceptron (MLP). We compare the obtained results between these two neural network architectures and found that the EFuNN is able to more accurately determine the rate of browning even when it has been exposed to a small number of images. The advantages of this method include the ability for this model to represent the important temporal information in a relatively compact neural network structure and to incrementally learn its input. Such an architecture reduces the added complexity of other neural network models that normally use recurrent connections in order to model the temporal dimension of the data

Topics: image recognition, wavelets, adaptive neural networks, on-line learning
Year: 2001
OAI identifier: oai:CiteSeerX.psu:10.1.1.18.8501
Provided by: CiteSeerX
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