Modeling and Simulation of Soft measurement Based on Improved BP Neural Network

DOI

10.2991/ifeesm-15.2015.141How to use a DOI?

Keywords

Back Propagation (BP), Principal Component Analysis (PCA), Genetic Algorithms (GA), Soft measurement.

Abstract

To solve the problem of Back Propagation (BP) neural network easy to get in local least value and the initial weight is chosen randomly, Principal Component Analysis (PCA) and Genetic Algorithms (GA) were introduced to the BP Neural Network to achieve their complementary advantages. Based on the BP neural Network a GA-BP neural Network improved network based on PCA is built and practically applied. The simulation results show that, the improved network could improve the generalization ability of the model and the ability to predict dynamic measurement data, which make the BP neural Network can be used even more widely.

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/).

Download article (PDF)

TY  - CONF
AU  - Xinchen Cui
AU  - Zhenlin Chen
PY  - 2015/09
DA  - 2015/09
TI  - Modeling and Simulation of Soft measurement Based on Improved BP Neural Network
BT  - Proceedings of the 2015 International Forum on Energy, Environment Science and Materials
PB  - Atlantis Press
SP  - 758
EP  - 761
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifeesm-15.2015.141
DO  - 10.2991/ifeesm-15.2015.141
ID  - Cui2015/09
ER  -