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

2008 ◽  
Author(s):  
Fu-Yuan Huang
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Fuzzy Neural Network ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Stock Market Prediction ◽  
Fuzzy Neural ◽  
Swarm Algorithm

The Application in Grain Monitoring Based on Particle Swarm-Fuzzy Neural Network Algorithm

2011 ◽  
Vol 474-476 ◽  
pp. 1116-1121 ◽  
Author(s):  
Zhen Tong ◽  
Wang Jing ◽  
Yi Ming ◽  
Jian Jun Wu
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Iterative Optimization ◽  
Network Algorithm ◽  
Temperature And Humidity ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Swarm Algorithm

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.

Tuning the Structure and Parameters of a Neural Network by Using Cooperative Quantum Particle Swarm Algorithm

2011 ◽  
Vol 48-49 ◽  
pp. 1328-1332 ◽  
Author(s):  
Qi Feng Tang ◽  
Liang Zhao ◽  
Rong Bin Qi ◽  
Hui Cheng ◽  
Feng Qian
Keyword(s):  
Neural Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Training Algorithms ◽  
Approximation Accuracy ◽  
Quantum Genetic Algorithm ◽  
Cooperation Mechanism ◽  
Swarm Algorithm ◽  
Redundant Structure

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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