Training and Application of Radial-Basis Process Neural Network Based on Improved Shuffled Flog Leaping Algorithm

DOI

10.2991/isci-15.2015.75How to use a DOI?

Keywords

Radial-basis process neural network;Shuffled frog leaping algorithm;Learning; algorithm;fault diagnosis

Abstract

A radial basis process neural networks can be established ,which is based on expanding the traditional radial basis function neural network to the time domain. Combined with the excellent characteristics of cloud model transformation between qualitative and quantitative , a improved shuffled flog leaping algorithm based on cloud model theory is presented. It is applied to training the radial basis process neural network .The neural network after optimization is used in pumping unit fault diagnosis. The diagnostic results between the CCHSFLA and BP algorithm were compared. The conclusion is that the RBPNN based on CCHSFLA has better training performance, faster convergence rate and higher accuracy.

Copyright

© 2015, 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  - Qiang Zhang
AU  - Li-jie Liu
PY  - 2015/01
DA  - 2015/01
TI  - Training and Application of Radial-Basis Process Neural Network Based on Improved Shuffled Flog Leaping Algorithm
BT  - Proceedings of the 2015 International Symposium on Computers & Informatics
PB  - Atlantis Press
SP  - 562
EP  - 568
SN  - 2352-538X
UR  - https://doi.org/10.2991/isci-15.2015.75
DO  - 10.2991/isci-15.2015.75
ID  - Zhang2015/01
ER  -