scholarly journals A hybrid appliance identification method by using grey relational artificial neural network

2020 ◽  
Vol 1 (2) ◽  
pp. 63-72
Author(s):  
Yılmaz Güven ◽  
Sıtkı Kocaoğlu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Identification Method ◽  
Artificial Neural ◽  
Grey Relational

The Tunnel Surrounding Rock Parameters Identification Method Based on PSO-ANN

2011 ◽  
Vol 94-96 ◽  
pp. 637-640 ◽  
Author(s):  
Zhan Ping Song ◽  
Song Bo Ren ◽  
Zhen Chao Guo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Surrounding Rock ◽  
Parameters Identification ◽  
Soil Parameters ◽  
Data Set ◽  
Identification Method ◽  
Artificial Neural ◽  
Rock Parameters ◽  
Rock And Soil

Aiming at the complexity and uncertainty of rock and soil body, the paper proposed a tunnel surrounding rock parameters identification method combining numerical simulation, particle swarm optimization and artificial neural network. The method acquired data set between rock soil parameters and monitoring displacement and trained artificial neural network. The analytical theory and method are introduced in detail, analyzes the tunnel of Dalian Metro by the proposed method, and gets satisfied results. Which states that the parameters identification method based on PSO-ANN is feasible and has good foreground.

A Computational Identification Method for GPI-Anchored Proteins by Artificial Neural Network

2012 ◽  
Vol 7 (2) ◽  
pp. 125-131 ◽  
Author(s):  
Yuri Mukai ◽  
Hirotaka Tanaka ◽  
Masao Yoshizawa ◽  
Osamu Oura ◽  
Takanori Sasaki ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Identification Method ◽  
Computational Identification ◽  
Artificial Neural

Application of Grey Relational Approach and Artificial Neural Network to Optimise Design Parameters of Bridge-Type Compliant Mechanism Flexure Hinge

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Huynh Ngoc Thai ◽  
Nguyen Quoc Manh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compliant Mechanism ◽  
Design Parameters ◽  
Output Displacement ◽  
Relational Analysis ◽  
Design Variables ◽  
Bridge Type ◽  
Artificial Neural ◽  
Grey Relational

The investigation proposed a hybrid Grey-artificial neural network to optimise the design parameters of a two degree of freedom (2-DOF) bridge-type compliant mechanism flexure hinge (BTCMFH). The design variables play a vital role in determining the deformation and stress of the mechanism. The investigation is different from the previous studies where the hybrid method is a combination of grey relational analysis and artificial neural network based on finite element method (FEM) in ANSYS to maximise output displacement (DI) and minimise the stress (ST) of the mechanism. The simulation and ANOVA results identified the design variables have significantly affected the output displacement and stress by their contribution. The grey relational analysis and artificial neural network predicted values are in agreement with the simulation results at optimal combination parameters with a deviation error displacement and stress being 0.57% and 2.1%, respectively. The optimal combination parameters with a deviation error of displacement and stress of 0.52% and 2.1%, respectively. The optimal values of DI and ST were obtained as 0.957 mm and 104.74 MPa, respectively. The optimal value of displacement amplification ratio gained is 95.7.

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