Prediction and Optimization of Rate of Penetration using a Hybrid Artificial Intelligence Method based on an Improved Genetic Algorithm and Artificial Neural Network

2020 ◽  
Author(s):  
Chengxi Li ◽  
Chris Cheng
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Improved Genetic Algorithm ◽  
Rate Of Penetration ◽  
Artificial Neural ◽  
Hybrid Artificial Intelligence

The Construction of Index System and Comprehensive Evaluation Model Based on Improved Genetic Algorithm and Fuzzy Neural Network

2010 ◽  
Vol 20-23 ◽  
pp. 1229-1235 ◽  
Author(s):  
Yuan Yuan Zhang ◽  
Shi Song Yang ◽  
Peng Dong
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Index System ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Back Propagation ◽  
Improved Genetic Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial neural network(ANN) and genetic algorithm (GA) have both prevalent uses in large area. Along with the development of technology a method based on the combination of Artificial neural network (ANN) and genetic algorithm (GA) aroused. In such a case, the paper uses the combination of Artificial neural network(ANN) and genetic algorithm (GA) to solve the problems of costructing index system and comprehensive evaluation. Firstly establishing feedforward neural network model and make sure about the input and output variables. Secondly improved genetic algorithm is used to solve the problem of network weight and threshold value which is constitute by three steps real codes, random selection and Genetic Manipulation of Chromosome. Moreover as it know to all, error back propagation(BP) algorithm is effective in local searching so adding error back propagation(BP) algorithm to genetic algorithm is a good way to get the satisfying result. Thirdly the paper gets the output of index effectiveness. Thirdly according to the entropy theory that the summation of effective value which could be involved in the index system should be larger than a certain critical value, the paper screened out the final index. Fourthly it uses the fuzzy neural network method to establishing the comprehensive evaluation model. Finally take the evaluation for teaching quality for example to authenticate the feasibility of the method.

Optimal Design for Mechanical Properties of Composite with Combination of Genetic Algorithm and Artificial Neural Network

2008 ◽  
Vol 368-372 ◽  
pp. 1645-1647 ◽  
Author(s):  
Fan Wei Zhang ◽  
Song Bang Zhou ◽  
Yue Zhang ◽  
Da Hai Zhang ◽  
Zhong Ping Li
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Optimal Design ◽  
Practical Application ◽  
Improved Genetic Algorithm ◽  
Fiber Density ◽  
Braided Composite ◽  
Artificial Neural

Improved genetic algorithm, combined with artificial neural network, is present for the optimal design of 2.5D braided composite. Dispersal simulation data, including maximal stresses and elastics properties, are adopted by artificial neural network for the calculation of strength property. Based on calculation method of strength mentioned above and other calculation models for other mechanical properties, genetic algorithm is employed for the design of structure parameters of 2.5D braided composite, such as wrap fiber density, fill fiber density and interface strength. These structure optimal parameters are finally optimized for practical application.

scholarly journals Modeling and Optimizing a New Culture Medium for In Vitro Rooting of G×N15 Prunus Rootstock using Artificial Neural Network-Genetic Algorithm

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Mohammad Mehdi Arab ◽  
Abbas Yadollahi ◽  
Maliheh Eftekhari ◽  
Hamed Ahmadi ◽  
Mohammad Akbari ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Culture Medium ◽  
In Vitro Rooting ◽  
Artificial Neural
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