Financial Security Evaluation in Power Production Industry Based on BP Neural Network Optimized by Genetic Algorithm

DOI

10.2991/amcce-15.2015.12How to use a DOI?

Keywords

financial security;power production industry; genetic algorithm; BP neural network; evaluation

Abstract

In recent years, power production industry investment and financing security issues have become increasingly prominent in China,which has brought serious financial security challenges to our country’s power production industry. Therefore, to evaluate the financial security is of vital significance.This paper selects genetic algorithms (GA) to optimize the traditional BP neural network algorithm,and establish BP neural network model improved by genetic algorithm. Using GA algorithm to optimize the connection weights and thresholds of BP neural network,giving full play to the global optimization capability of GA algorithm and local search advantage of BP algorithm.So,it can be a good way to overcome the problem of BP neural network--weights of which are random.Through the practical experiments,the results indicate that prediction results based on BP neural network optimized by genetic algorithm,can evaluate the financial security of Chinese power production industry effectively.Moreover,the operation process of the model not only has a fast convergence rate,but also the prediction results has high 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  - Jiao Lin
AU  - Yao Shuo
AU  - Jiao Di
PY  - 2015/04
DA  - 2015/04
TI  - Financial Security Evaluation in Power Production Industry Based on BP Neural Network Optimized by Genetic Algorithm
BT  - Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering
PB  - Atlantis Press
SP  - 65
EP  - 69
SN  - 1951-6851
UR  - https://doi.org/10.2991/amcce-15.2015.12
DO  - 10.2991/amcce-15.2015.12
ID  - Lin2015/04
ER  -