Severe Convection Weather Identification Model Based on Rough Set Theory and Artificial Fish Swarm Algorithm

2021 ◽  
Author(s):  
Jianlin Guo ◽  
Zhiying Lu ◽  
Chenyi Wang ◽  
Haopeng Wu ◽  
Xudong Ding
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Artificial Fish Swarm Algorithm ◽  
Model Based ◽  
Severe Convection ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Identification Model

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.

scholarly journals Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network

2019 ◽  
Vol 11 (17) ◽  
pp. 4513 ◽  
Author(s):  
Xiaoqing Li ◽  
Qingquan Jiang ◽  
Maxwell K. Hsu ◽  
Qinglan Chen
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Assessment Model ◽  
Software Project ◽  
Risk Assessment Model ◽  
Backpropagation Neural Network ◽  
Model Based ◽  
Sample Data

Software supports continuous economic growth but has risks of uncertainty. In order to improve the risk-assessing accuracy of software project development, this paper proposes an assessment model based on the combination of backpropagation neural network (BPNN) and rough set theory (RST). First, a risk list with 35 risk factors were grouped into six risk categories via the brainstorming method and the original sample data set was constructed according to the initial risk list. Subsequently, an attribute reduction algorithm of the rough set was used to eliminate the redundancy attributes from the original sample dataset. The input factors of the software project risk assessment model could be reduced from thirty-five to twelve by the attribute reduction. Finally, the refined sample data subset was used to train the BPNN and the test sample data subset was used to verify the trained BPNN. The test results showed that the proposed joint model could achieve a better assessment than the model based only on the BPNN.

The Applications Based on the Rough Set Theory Algorithms to Determine the Weight

2014 ◽  
Vol 989-994 ◽  
pp. 1736-1738
Author(s):  
Ning Ning Chen
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Model Based

This paper puts forward on the Rough Set theory algorithms, and combined it with the method of determining the index weights, then established a weight determined algorithm model based on rough set, Thereby reducing the influence of subjective factors on the weight determination.

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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