Research on Nonlinear Approximation Model of Radial Basis Function Neural Network Trained Using Artificial Fish Swarm Algorithm with Adaptive Adjustment

2015 ◽  
Vol 713-715 ◽  
pp. 1855-1858 ◽  
Author(s):  
Xu Sheng Gan ◽  
Xue Qin Tang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Nonlinear Approximation ◽  
Nonlinear Function ◽  
Training Algorithm ◽  
Step Size ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Adaptive Adjustment ◽  
Swarm Algorithm

In order to improve the modeling efficiency of RBF neural network, an Artificial Fish Swarm Algorithm (AFSA) training algorithm with an adaptive mechanism is proposed. In the training algorithm, the search step size and visible domain of AFSA algorithm can be adjusted dynamically according to the convergence characteristics of artificial fish swarm, and then the improved AFSA algorithm is used to optimize the parameters of RBF neural network. The example shows that, the proposed model is a better approximation performance for the nonlinear function.

Neural Network Algorithm Based Method for Stock Price Trend Prediction

2013 ◽  
Vol 13 (22) ◽  
pp. 5384-5390 ◽  
Author(s):  
Nan Ma ◽  
Yun Zhai ◽  
Wen-Fa Li ◽  
Cui-Hua Li ◽  
Shan-shan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Stock Price ◽  
Trend Prediction ◽  
Price Trend ◽  
Network Algorithm ◽  
Neural Network Algorithm
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