Research on Nonlinear Identification Model of Wavelet Neural Network Trained by Artificial Fish Swarm Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 1920-1923
Author(s):  
Xu Sheng Gan ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Wavelet Neural Network ◽  
Nonlinear Identification ◽  
Nonlinear Characteristics ◽  
Artificial Fish Swarm Algorithm ◽  
Model Based ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Identification Model

To describe the complex nonlinear characteristics of a system accurately, a Wavelet Neural Network (WNN) identification model based on Artificial Fish Swarm (AFS) algorithm is proposed. In the identification model, AFS algorithm is introduced to optimize the parameters combination of the network for the satisfactory WNN model. The simulation shows that, the proposed method is a good nonlinear identification capability, and is feasible to identify the nonlinear system.

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.

Tree hierarchical directed evacuation network model based on artificial fish swarm algorithm

2019 ◽  
Vol 30 (11) ◽  
pp. 1950097 ◽  
Author(s):  
Xinlu Zong ◽  
Chunzhi Wang ◽  
Jiayuan Du ◽  
Yingli Jiang
Keyword(s):  
Emergency Evacuation ◽  
Path Selection ◽  
Selection Strategy ◽  
Public Places ◽  
Artificial Fish Swarm Algorithm ◽  
Model Based ◽  
Straight Line Distance ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Evacuation Routes

With the increase of emergencies in large public places, emergency evacuation research has become an important and urgent issue. This paper first proposes a tree hierarchical evacuation network. According to the hierarchical path selection strategy, the evacuation routes are obtained and sorted by the length of route. This hierarchical path selection strategy is more realistic than using the straight line distance. An evacuation model based on hierarchical directed evacuation network is presented in this paper, and a hierarchical directed artificial fish swarm algorithm is proposed to solve the evacuation problem. The model simulates the movements of pedestrians by means of preying, swarming, following and waiting behaviors of artificial fish swarm algorithm. During the evacuation process, the effects of congestion, retrograde and blocking time on evacuation speed and route selection are considered. The simulation results show that the proposed model and algorithm can effectively improve the evacuation efficiency in a stadium, and provide scientific and reasonable path guidance.

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