@inproceedings{79a2b44141594188bc8ebfdb560a1877,
title = "Chaotic neural networks for multi-resolution analysis",
abstract = "In this paper, we investigate new dynamic neural networks for brain data multi-resolution analysis. It is based on chaotic neuron model. Multi-resolution chaotic neural network (MRCNN) architecture is built by cascading the single-layer neural sub-networks, and a higher layer learns to cluster the prototypes developed at the layer directly below it. They have multi-output in coarse-to-fine hierarchical manner, which can reveal the inherent structural characteristic of their input data. A learning processing is also derived from training weights of the networks. They are availably applied to brain data analysis.",
keywords = "Chaotic Neuron Model, Multi-Resolution, Neural Network",
author = "Liu, {Hong Bo} and Wang, {Xiu Kun} and Tang, {Yi Yuan} and Zhang, {Shao Zhong}",
year = "2003",
language = "English (US)",
isbn = "0780378652",
series = "International Conference on Machine Learning and Cybernetics",
pages = "1102--1105",
booktitle = "International Conference on Machine Learning and Cybernetics",
note = "International Conference on Machine Learning and Cybernetics ; Conference date: 02-11-2003 Through 05-11-2003",
}