Proceedings of the 2012 National Conference on Information Technology and Computer Science

A Train Algorithm of Multi-Aggregation Process Neural Networks Based on Chaos Genetic Optimization

Authors
Yue-Wu Pang, Shao-Hua XU
Corresponding Author
Yue-Wu Pang
Available Online November 2012.
DOI
https://doi.org/10.2991/citcs.2012.147How to use a DOI?
Keywords
process neural networks, train algorithm, chaos genetic algorithm, mixed optimization strategy
Abstract
Aiming at the learning problem of multi-aggregation process neural networks, an optimization train method based on chaos genetic algorithm (CGA) with lowest mean square algorithm (LMS) is proposed in the paper. The learning problem of the network weight functions is converted to the training of basis expansion coefficients through adopting the function orthogonal basis expansion method. The lowest mean square error as the train objective function, the global optimal solution of network parameters is solved in feasible solution space using the chaos rail to traverse the search of CGA. The application in multi-variant dynamic signal identification proved that the algorithm proposed in the paper improved the training efficiency and stability greatly
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Proceedings
2012 National Conference on Information Technology and Computer Science
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2012
ISBN
978-94-91216-39-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/citcs.2012.147How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yue-Wu Pang
AU  - Shao-Hua XU
PY  - 2012/11
DA  - 2012/11
TI  - A Train Algorithm of Multi-Aggregation Process Neural Networks Based on Chaos Genetic Optimization
BT  - 2012 National Conference on Information Technology and Computer Science
PB  - Atlantis Press
SP  - 571
EP  - 574
SN  - 1951-6851
UR  - https://doi.org/10.2991/citcs.2012.147
DO  - https://doi.org/10.2991/citcs.2012.147
ID  - Pang2012/11
ER  -