Integration of an Improved Particle Swarm Algorithm and Fuzzy Neural Network for Shanghai Stock Market Prediction

Author(s):  
Fu-Yuan Huang
2011 ◽  
Vol 474-476 ◽  
pp. 1116-1121 ◽  
Author(s):  
Zhen Tong ◽  
Wang Jing ◽  
Yi Ming ◽  
Jian Jun Wu

In order to strengthen the administration of the barn, we need to storage environment for scientific and effective monitoring and analysis. This paper firstly filter and merge the temperature and humidity information in the environment by using fuzzy neural network, then regard the temperature and humidity as particles, calculate the fitness of the particles according to corresponding mildew rate and cost, finally the outputs of temperature and humidity curve values are obtained through iterative optimization of local and global extreme by particle swarm algorithm.


2011 ◽  
Vol 48-49 ◽  
pp. 1328-1332 ◽  
Author(s):  
Qi Feng Tang ◽  
Liang Zhao ◽  
Rong Bin Qi ◽  
Hui Cheng ◽  
Feng Qian

In this paper, a cooperative quantum genetic algorithm-particle swarm algorithm (CQGAPSO) is applied to tune both structure and parameters of a feedforward neural network (NN) simultaneously. In CQGAPSO algorithm, QGA is used to optimize the network structure and PSO algorithm is employed to search the parameters space. The amplitude-based coding method and cooperation mechanism improve the learning efficiency, approximation accuracy and generalization of NN. Furthermore, the ill effects of approximation ability caused by redundant structure of NN are eliminated by CQGAPSO. The experimental results show that the proposed method has better prediction accuracy and robustness in forecasting the sunspot numbers problems than other training algorithms in the literatures.


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