Study on Improved Flexible Neural tree Optimization Algorithm

DOI

10.2991/isccca.2013.167How to use a DOI?

Keywords

flexible neural tree, fitness, Parameter optimization, multi expression programming optimization.

Abstract

The BP neural network is easy to fall into local minimum point, the algorithm convergence speed slow, this paper puts forward an improved algorithm of flexible neural tree, introduced the basic theory knowledge of Flexible neural tree , analyzes the characteristics and advantages of the neural tree. The structure optimization and parameter optimization are adopted some optimization algorithm, Introduced the multi expression programming algorithm for optimization of flexible neural tree structure and by using the improved particle swarm algorithm to optimize the parameters of flexible neural tree, Finally the establishment of complete flexible neural tree model.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yu Wang
PY  - 2013/02
DA  - 2013/02
TI  - Study on Improved Flexible Neural tree Optimization Algorithm
BT  - Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation (ISCCCA 2013)
PB  - Atlantis Press
SP  - 668
EP  - 671
SN  - 1951-6851
UR  - https://doi.org/10.2991/isccca.2013.167
DO  - 10.2991/isccca.2013.167
ID  - Wang2013/02
ER  -