Surface roughness prediction based upon experimental design and neural network models

2003 ◽  
Author(s):  
N. Ben Fredj ◽  
R. Amamou ◽  
M.A. Rezgui
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Experimental Design ◽  
Network Models ◽  
Neural Network Models ◽  
Surface Roughness Prediction ◽  
Roughness Prediction

scholarly journals Towards Enhanced Performance of Neural-Network-Based Fault Detection Using an Sequential D-Optimum Experimental Design

2018 ◽  
Vol 8 (8) ◽  
pp. 1290 ◽  
Author(s):  
Beata Mrugalska
Keyword(s):  
Neural Network ◽  
Experimental Design ◽  
Fault Detection ◽  
Network Models ◽  
Neural Model ◽  
Neural Network Models ◽  
Optimum Experimental Design ◽  
The Neural Network ◽  
Adaptive Thresholds ◽  
Linear Neural Network

Increasing expectations of industrial system reliability require development of more effective and robust fault diagnosis methods. The paper presents a framework for quality improvement on the neural model applied for fault detection purposes. In particular, the proposed approach starts with an adaptation of the modified quasi-outer-bounding algorithm towards non-linear neural network models. Subsequently, its convergence is proven using quadratic boundedness paradigm. The obtained algorithm is then equipped with the sequential D-optimum experimental design mechanism allowing gradual reduction of the neural model uncertainty. Finally, an emerging robust fault detection framework on the basis of the neural network uncertainty description as the adaptive thresholds is proposed.

Sign in / Sign up

Export Citation Format

Share Document