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Advances in Nano Research
  Volume 11, Number 1, July 2021 , pages 083-99
DOI: https://doi.org/10.12989/anr.2021.11.1.083
 


Computer simulation for stability performance of sandwich annular system via adaptive tuned deep learning neural network optimization
Yan Ming, Yousef Zandi, Morteza Gholizadeh, Khaled Oslub, Mohamed Amine Khadimallah and Alibek Issakhov

 
Abstract
    In this article with the aid of adaptively tuned deep neural network (DNN), dynamic stability analysis of the sandwich structure has been investigated. Due to finding the design-points features, the numerical solution procedure called two-dimensional generalized differential quadrature technique has been applied to the ordinary differential equations of the current structure system acquired on the foundation of the kinematic theory with refined higher order terms. Also, the involved parameters with the optimum values in the fully-connected neural network mechanism are obtained via momentum-based optimizer. For modeling a moderately thick structure, higher order terms of shear deformation are chosen. For stability analysis of the current structure the design points considering the method of adaptive learning is presented. For analysis of the current structure
 
Key Words
    DNN, GDQM, honeycomb core, frequency characteristic, sandwich disk
 
Address
Yan Ming: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China

Yousef Zandi and Morteza Gholizadeh: Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

Khaled Oslub: Faculty of Mechanical Engineering, Tabriz University, Tabriz, Iran

Mohamed Amine Khadimallah: Prince Sattam Bin Abdulaziz University, College of Engineering, Civil Engineering Department, Al-Kharj, 16273, Saudi Arabia/ Laboratory of Systems and Applied Mechanics, Polytechnic School of Tunisia, University of Carthage, Tunis, Tunisia

Alibek Issakhov: Al-Farabi Kazakh National University, Almaty, Kazakhstan/ Kazakh-British Technical University, Almaty, Kazakhstan

 

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