Design optimization of erbium-doped fiber amplifiers using artificial neural networks

2008 ◽  
Vol 47 (8) ◽  
pp. 085008 ◽  
Author(s):  
Narayanan Vijayakumar
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Fiber Amplifiers ◽  
Erbium Doped ◽  
Artificial Neural

Design optimization of electromagnetic devices using artificial neural networks

1992 ◽  
Vol 28 (5) ◽  
pp. 2805-2807 ◽  
Author(s):  
O.A. Mohammed ◽  
D.C. Park ◽  
F.G. Uler ◽  
C. Ziqiang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Electromagnetic Devices ◽  
Artificial Neural

Improved Multidisciplinary Design Optimization Based on Genetic Algorithm and Artificial Neural Networks and Its Application

2016 ◽  
Vol 13 (10) ◽  
pp. 6501-6508
Author(s):  
Yi Su ◽  
Fa-Yin Wang ◽  
Jian-Yu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Computational Cost ◽  
System Level ◽  
Multidisciplinary Design ◽  
Local Optimum ◽  
Artificial Neural

Multidisciplinary Design Optimization (MDO) is an algorithm widely used in the engineering field currently. However, traditional MDO often leads to the failure of convergence or local optimum problems caused by convergence. In such cases, a multidisciplinary design optimization based on genetic algorithm (GA) and artificial neural networks (ANN) (GA-ANN-MDO) is presented in the paper. Under the thought of parallel distribution of traditional MDO, the real sub-disciplinary model is replaced by a highly precise ANN model dependent on the Latin Hypercube experimental design method in the GA-ANN-MDO, so as to reduce the computational cost and smooth the value noise. The GA optimization system level is applied to decline the possibility of partial solution involved in the optimization. As shown from the optimization results of two classic mathematical examples, GA-ANN-MDO is presented good robustness, which could quickly and effectively converge to the global optimal solution. In addition, a project example was employed finally to verify the feasibility of GA-ANN-MDO in the engineering.

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