Recognition of Fatigue Failure in Wave Energy Converter Using Statistical Control Chart, Multi-criteria Decision Making Tools and Polynomial Neural Network Model

2021 ◽  
pp. 259-270
Author(s):  
Satyabrata Saha
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Control Chart ◽  
Network Model ◽  
Neural Network Model ◽  
Wave Energy ◽  
Wave Energy Converter ◽  
Multi Criteria Decision Making ◽  
Statistical Control ◽  
Polynomial Neural Network

INDUCING DYNAMIC RULES OF NATIONS' COMPETITIVENESS FROM 2001–2005 MCI-WCY

2009 ◽  
Vol 08 (03) ◽  
pp. 549-580 ◽  
Author(s):  
HAN-LIN LI ◽  
YU-CHIEN KO
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Regression Model ◽  
Network Model ◽  
Neural Network Model ◽  
Optimization Model ◽  
Business Decision ◽  
The World

A nation's competitiveness has become more and more important in forming government strategy and business decision making. This study proposes an optimization model, instead of regression model or neural network model, to induce rules for dynamic nations' competitiveness based on the Major Competitiveness Indicators of the World Competitiveness Yearbook. Fourteen attributes are used to form the dynamic rules expressed in "IF…THEN…" forms. According to the induced rules, the strategic implications are suggested for various groups of nations to improve or to sustain their competitiveness.

scholarly journals Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 381 ◽  
Author(s):  
Jong-Min Kim ◽  
Ning Wang ◽  
Yumin Liu ◽  
Kayoung Park
Keyword(s):  
Neural Network ◽  
Control Chart ◽  
Network Model ◽  
Neural Network Model ◽  
Multivariate Data ◽  
Principal Component ◽  
Binary Response ◽  
Link Functions ◽  
The Neural Network ◽  
Input Variables

Quality control studies have dealt with symmetrical data having the same shape with respect to left and right. In this research, we propose the residual (r) control chart for binary asymmetrical (non-symmetric) data with multicollinearity between input variables via combining principal component analysis (PCA), functional PCA (FPCA) and the generalized linear model with probit and logit link functions, and neural network regression model. The motivation in this research is that the proposed control chart method can deal with both high-dimensional correlated multivariate data and high frequency functional multivariate data by neural network model and FPCA. We show that the neural network r control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits.

scholarly journals Decision making under uncertainty in a spiking neural network model of the basal ganglia

2016 ◽  
Vol 15 (04) ◽  
pp. 515-538 ◽  
Author(s):  
Charlotte Héricé ◽  
Radwa Khalil ◽  
Marie Moftah ◽  
Thomas Boraud ◽  
Martin Guthrie ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Basal Ganglia ◽  
Network Model ◽  
Neural Network Model ◽  
Decision Making Under Uncertainty ◽  
Spiking Neural Network ◽  
Spiking Neural Network Model
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