Freight Prediction Based on BP Neural Network Improved by Chaos Artificial Fish-Swarm Algorithm

2008 ◽  
Author(s):  
Yuansheng Huang ◽  
Yufang Lin
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

scholarly journals Modeling FOG Drift Using Back-Propagation Neural Network Optimized by Artificial Fish Swarm Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Rui Song ◽  
Xiyuan Chen ◽  
Chong Shen ◽  
Hong Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Fiber Optic Gyroscope ◽  
Temperature Drift ◽  
Error Sources ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Based on the temperature drift characteristic of fiber optic gyroscope (FOG), a novel modeling and compensation method which integrated the artificial fish swarm algorithm (AFSA) and back-propagation (BP) neural network is proposed to improve the output accuracy of FOG and the precision of inertial navigation system. In this paper, AFSA is used to optimize the weights and threshold of BP neural network which determine precision of the model directly. In order to verify the effectiveness of the proposed algorithm, the predicted results of BP optimized by genetic algorithm (GA) and AFSA are compared and a quantitative evaluation of compensation results is analyzed by Allan variance. The comparison result illustrated the main error sources and the sinusoidal noises in the FOG output signal are reduced by about 50%. Therefore, the proposed modeling method can be used to improve the FOG precision.

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.

Sign in / Sign up

Export Citation Format

Share Document