GaN power amplifiers design using genetic neural network model

Author(s):  
Anwar H. Jarndal ◽  
Omar A. Alhammadi ◽  
Rashid H. Al-Ali
Author(s):  
Xiaofei Yu ◽  
Xin Hu ◽  
Zhijun Liu ◽  
Chaowei Wang ◽  
Weidong Wang ◽  
...  

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 ◽  
...  

2014 ◽  
Vol 933 ◽  
pp. 921-925
Author(s):  
Xin Yun Liu ◽  
Heng Jun Liu

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.


2005 ◽  
Vol 02 (01) ◽  
pp. 37-43
Author(s):  
JUNJIE CHEN ◽  
WEIYI HUANG

Genetic neural network model of solving the problem of nonlinearity rectification of sensor systems, is put forward in the light of the shortcomings of least square and other conventional methods. And in theory the model is emphatically expounded. Computer simulations are presented to demonstrate that approximation accuracy of the model is far higher than the conventional least square method and the model possesses stronger robustness through adopting the methods in this paper. The research in the paper indicates that the model can also be used to realize nonlinearity rectification in other similar systems.


2003 ◽  
Vol 95 (1) ◽  
pp. 71 ◽  
Author(s):  
Stephen M. Welch ◽  
Judith L. Roe ◽  
Zhanshan Dong

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