Shunt Control on Smart Structures Using Genetic Algorithm and Neural Network Method.

2019 ◽  
Author(s):  
Venicio Silva Araujo ◽  
Guilherme Silva Prado ◽  
Heinsten Frederich Leal dos Santos
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Smart Structures ◽  
Neural Network Method ◽  
Network Method ◽  
Shunt Control

Performance Prediction of Pre-Corroded Aluminum Alloy Using Genetic Algorithm-Neural Network and Fuzzy Neural Network

2008 ◽  
Vol 33-37 ◽  
pp. 1283-1288 ◽  
Author(s):  
Chao Hua Fan ◽  
Yu Ting He ◽  
Hong Peng Li ◽  
Feng Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Aluminum Alloy ◽  
Aluminum Alloys ◽  
Performance Prediction ◽  
Bp Neural Network ◽  
Fuzzy Neural Network ◽  
Neural Network Method ◽  
Network Method ◽  
Fuzzy Neural

Genetic algorithm is introduced in the study of network authority values of BP neural network, and a GA-NN algorithm is established. Based on this genetic algorithm-neural network method, a predictive model for fatigue performances of the pre-corroded aluminum alloys under a varied corrosion environmental spectrum was developed by means of training from the testing dada. At the same time, a fuzzy-neural network method is established for the same purpose. The results indicate that genetic algorithm-neural network and fuzzy-neural network can both be employed to predict the underlying fatigue performances of the pre-corroded aluminum alloy precisely.

Prediction of soil wind erodibility using a hybrid Genetic algorithm – Artificial neural network method

CATENA ◽  
2020 ◽  
Vol 187 ◽  
pp. 104315 ◽  
Author(s):  
I. Kouchami-Sardoo ◽  
H. Shirani ◽  
I. Esfandiarpour-Boroujeni ◽  
A.A. Besalatpour ◽  
M.A. Hajabbasi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Hybrid Genetic Algorithm ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

A Novel Improved Genetic Algorithm and Application in Mechanical Optimal Design

2009 ◽  
Vol 628-629 ◽  
pp. 263-268 ◽  
Author(s):  
Zhong Min Wang ◽  
Yu Jun Cai ◽  
D.H. Miao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimal Design ◽  
Hybrid Genetic Algorithm ◽  
Pso Algorithm ◽  
Improved Genetic Algorithm ◽  
Neural Network Method ◽  
Random Direction ◽  
Network Method ◽  
Species Population

A new improved genetic algorithm (IGA) based on floating point encoding is proposed. Firstly, IGA uses information entropy to produce better initialized species population. Secondly, after synthetically studying the searching properties of crossover operator in GA, it designs a new crossover strategy that effectively increases searching efficiencies of IGA. Thirdly, to avoid searching being trapped in local minimum, it designs a chaos degenerate mutation operator that makes the searching fast converge to a global minimum. At last IGA is used to solve the problem of the optimal design to crane girder, which is a typical problem of mechanical optimal design. Compared with the traditional random direction method, neural network method, genetic-neural network method, hybrid genetic algorithm, chaos-GA, PSO algorithm, chaos-PSO algorithm and standard GA, IGA shows better performance at the aspect of solution precision and convergence speed than that of these algorithms.

Predictive Model Based on Genetic Algorithm-Neural Network for Fatigue Performances of Pre-Corroded Aluminum Alloys

2007 ◽  
Vol 353-358 ◽  
pp. 1029-1032 ◽  
Author(s):  
Chao Hua Fan ◽  
Yu Ting He ◽  
Heng Xi Zhang ◽  
Hong Peng Li ◽  
Feng Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Aluminum Alloy ◽  
Aluminum Alloys ◽  
Predictive Model ◽  
Bp Neural Network ◽  
Neural Network Method ◽  
Network Algorithm ◽  
Network Method ◽  
Neural Network Algorithm

In the paper, genetic algorithm is introduced in the study of network authority values of BP neural network, and a GA-NN algorithm is established. Based on this genetic algorithm-neural network method, a predictive model for fatigue performances of the pre-corroded aluminum alloys under a varied corrosion environmental spectrum was developed by means of training from the testing dada, and the fatigue performances of pre-corroded aluminum alloys can be predicted. The results indicate that genetic algorithm-neural network algorithm can be employed to predict the underlying fatigue performances of the pre-corroded aluminum alloy precisely, compared with traditional neural network.

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