GMDH-type neural network modeling and genetic algorithm-based multi-objective optimization of thermal and friction characteristics in heat exchanger tubes with wire-rod bundles

2015 ◽  
Vol 52 (8) ◽  
pp. 1585-1593 ◽  
Author(s):  
Masoud Rahimi ◽  
Reza Beigzadeh ◽  
Mehdi Parvizi ◽  
Smith Eiamsa-ard
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Heat Exchanger ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Multi Objective Optimization ◽  
Rod Bundles ◽  
Friction Characteristics ◽  
Wire Rod ◽  
Multi Objective

A study on global optimization and deep neural network modeling method in performance-seeking control

2019 ◽  
Vol 234 (1) ◽  
pp. 46-59 ◽  
Author(s):  
Qiangang Zheng ◽  
Dawei Fu ◽  
Yong Wang ◽  
Haoying Chen ◽  
Haibo Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Global Optimization ◽  
Interval Analysis ◽  
Sequential Quadratic Programming ◽  
Deep Neural Network ◽  
Network Modeling ◽  
Engine Performance ◽  
Neural Network Modeling ◽  
Swarm Optimization

In this article, a novel performance-seeking control method based on deep neural network and interval analysis is proposed to obtain a better engine performance. A deep neural network modeling method which has stronger representation capability than conventional neural network and can deal with big training data is adopted to establish an on-board model in the subsonic and supersonic cruising envelops. Meanwhile, a global optimization algorithm interval analysis is applied here to get a better engine performance. Finally, two simulation experiments are conducted to verify the effectiveness of the proposed methods. One is the on-board model modeling which compares the deep neural network with the conventional neural network, and the other is the performance-seeking control simulations comparing interval analysis with feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm, respectively. These two experiments show that the deep neural network has much higher precision than the conventional neural network and the interval analysis gets much better engine performance than feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm.

scholarly journals Modeling of multicomponent diffraction structures based on optimization approaches and numerical methods

2019 ◽  
Vol 30 ◽  
pp. 05013
Author(s):  
Igor Lvovich ◽  
Yakov Lvovich ◽  
Andrey Preobrazhenskiy ◽  
Oleg Choporov
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Numerical Methods ◽  
Network Modeling ◽  
Methodological Approach ◽  
Neural Network Modeling ◽  
Expert Evaluation ◽  
Two Dimensional ◽  
Modeling Expert

The paper proposes a methodological approach in which the representation of objects in the form of a set of diffraction structures is their Association into groups. Using neural network modeling, expert evaluation and application of optimization based on genetic algorithm, there is a formation of the object with the desired scattering properties. An example of modeling an object presented as a set of two-dimensional cylinders is given.

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