Applications Based on Genetic Neural Network Model of Lianyungang Marine Water Quality Optimization Techniques and Algorithms Technology

2010 ◽  
Author(s):  
Wenbin Chen ◽  
Weixing Ma
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Network Model ◽  
Neural Network Model ◽  
Optimization Techniques ◽  
Marine Water ◽  
Quality Optimization ◽  
Genetic Neural Network

Parameter Optimization of High Deposition Rate Laser Cladding Based on the Response Surface Method and Genetic Neural Network Model

2021 ◽  
Vol 48 (6) ◽  
pp. 0602112
Author(s):  
庞祎帆 Pang Yifan ◽  
傅戈雁 Fu Geyan ◽  
王明雨 Wang Mingyu ◽  
龚燕琪 Gong Yanqi ◽  
余司琪 Yu Siqi ◽  
...  
Keyword(s):  
Neural Network ◽  
Response Surface ◽  
Response Surface Method ◽  
Deposition Rate ◽  
Network Model ◽  
Parameter Optimization ◽  
Neural Network Model ◽  
High Deposition Rate ◽  
Surface Method ◽  
Genetic Neural Network

Genetic Neural Network Based Financial Distress Prediction

2014 ◽  
Vol 933 ◽  
pp. 921-925
Author(s):  
Xin Yun Liu ◽  
Heng Jun Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Financial Distress ◽  
Structural Parameters ◽  
Optimal Structure ◽  
Structure Parameters ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Genetic Neural Network

Enterprise financial distress prediction based on neural network has some disadvantages, such as complex structure, slow convergence rate and easily falling into local minimum points. The paper presents the genetic neural network based enterprise financial distress prediction. Firstly, the structural parameters of neural network model are encoded and connected into gene sequence to obtain an individual. A certain number of individuals make up a population. Secondly, after the reproduction, crossover and mutation operations upon the population, the best individual, that is the optimal structure parameters of neural network model, is obtained. Finally, the neural network model with the optimal structure parameters is trained by the training samples and the trained neural network model can realize enterprise financial distress prediction. The testing results show that the method achieves higher training speed and lower error rate.

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