Reliability Inference for Products of Weibull Distribution by Using Artificial Neural Network Formal Description

2006 ◽  
Vol 326-328 ◽  
pp. 573-576
Author(s):  
Yung Chung Chen ◽  
Pei Hsi Lee ◽  
Chien Ming Chen
Keyword(s):  
Weibull Distribution ◽  
Research Result ◽  
Back Propagation ◽  
Accelerated Life Testing ◽  
Learning Ability ◽  
Accelerated Life ◽  
Input Variables ◽  
Hidden Layer ◽  
Propagation Network ◽  
Self Learning

Back-propagation network (BPN) has the advantage of simulating a nonlinear system that is difficult to describe by a physical model. This study introduces a back-propagation network methodology to estimate the accelerated life reliability. The environmental stresses and failure times are chosen as the input variables. An optimum prediction system is acquired by adjusting the number of neurons in the hidden layer and the output layer of neural networks. For a numerical example, the developed BPN architecture is applied to real accelerated life testing data of the STNLCD modules which are distributed as a Weibull distribution. By the research result, we can have the conclusion that the BPN methodology is practical to make the reliability inference with the advantages of self-learning ability even without mathematics models.

An intelligent system for selection of grinding wheels

1997 ◽  
Vol 211 (8) ◽  
pp. 635-641 ◽  
Author(s):  
Y Li ◽  
B Mills ◽  
W B Rowe
Keyword(s):  
Neural Network ◽  
Intelligent System ◽  
Back Propagation ◽  
Grinding Wheel ◽  
Neural Network System ◽  
Grinding Conditions ◽  
Training Examples ◽  
Using Data ◽  
Hidden Layer ◽  
Propagation Network

This paper describes the development of a neural network system for grinding wheel selection. The system employs a back-propagation network with one hidden layer and was trained using data from reference handbooks. It is shown that a neural network is capable of learning the relationship between the wheel and the grinding process without a requirement for rules or equations. It was further found that a relatively small number of training examples allows the system to produce reliable recommendations for a much greater number of combinations of grinding conditions. The system was developed on a PC using the C++ programming language.

Application of Improved BP Neural Network in the Preparation Processing of the CaCO3 Nanocrystalline

2017 ◽  
Vol 726 ◽  
pp. 338-342 ◽  
Author(s):  
Qiang Luo ◽  
Qing Li Ren
Keyword(s):  
Bp Neural Network ◽  
Layer Structure ◽  
Back Propagation ◽  
Raw Material ◽  
Technological Factors ◽  
Learning Speed ◽  
Main Component Analysis ◽  
Input Variables ◽  
Main Component ◽  
Propagation Network

A three-layer structure back-propagation network model based on the non-linear relationship between the size of the CaCO3 nanocrystalline and the technological factors, such as reaction time, reaction temperature, raw material adding amount of NaCO3 and CaCl2, was established. Moreover, in order to accelerate the converging rate and avoid the non-converging situation, the momentum terms are introduced. Besides, the variable learning speed is adopted. At the same time, the input variables were pretreated by using the main component analysis firstly. And the results show that the improved back propagation neural networks model is very efficient for predication of the CaCO3 nanocrystalline size.

scholarly journals Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 936
Author(s):  
Simona Moldovanu ◽  
Cristian-Dragos Obreja ◽  
Keka C. Biswas ◽  
Luminita Moraru
Keyword(s):  
Test Performance ◽  
Structural Design ◽  
Back Propagation ◽  
Skin Lesions ◽  
Public Image ◽  
Data Sets ◽  
Dysplastic Nevus ◽  
Melanoma Detection ◽  
Hidden Layer ◽  
Propagation Network

In the automatic detection framework, there have been many attempts to develop models for real-time melanoma detection. To effectively discriminate benign and malign skin lesions, this work investigates sixty different architectures of the Feedforward Back Propagation Network (FFBPN), based on shape asymmetry for an optimal structural design that includes both the hidden neuron number and the input data selection. The reason for the choice of shape asymmetry was based on the 5–10% disagreement between dermatologists regarding the efficacy of asymmetry in the diagnosis of malignant melanoma. Asymmetry is quantified based on lesion shape (contour), moment of inertia of the lesion shape and histograms. The FFBPN has a high architecture flexibility, which indicates it as a favorable tool to avoid the over-parameterization of the ANN and, equally, to discard those redundant input datasets that usually result in poor test performance. The FFBPN was tested on four public image datasets containing melanoma, dysplastic nevus and nevus images. Experimental results on multiple benchmark data sets demonstrate that asymmetry A2 is a meaningful feature for skin lesion classification, and FFBPN with 16 neurons in the hidden layer can model the data without compromising prediction accuracy.

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