Computer simulation for stability performance of sandwich annular system via adaptive tuned deep learning neural network optimization
Ming Y. Zandi Y. Gholizadeh M. Oslub K. Khadimallah M.A. Issakhov A.
July 2021Techno-Press
Advances in Nano Research
2021#11Issue 183 - 99 pp.
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 twodimensional 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 ‘accuracy (used for determining the design-points) is presented through than the published outcomes in the literature. The outcomes of accuracy section of the current research show that the DNN-based model in analysis of the sandwich structure has less error than other models. The results show that the current momentum-based optimizer can be good tool for future researches about stability analysis of the various structure due to its good accuracy.
DNN , frequency characteristic , GDQM , honeycomb core , sandwich disk
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School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Faculty of Mechanical Engineering, Tabriz University, Tabriz, Iran
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
Al-Farabi Kazakh National University, Almaty, Kazakhstan
Kazakh-British Technical University, Almaty, Kazakhstan
School of Mechatronic Engineering and Automation
Department of Civil Engineering
Faculty of Mechanical Engineering
Prince Sattam Bin Abdulaziz University
Laboratory of Systems and Applied Mechanics
Al-Farabi Kazakh National University
Kazakh-British Technical University
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