A Study on a New Method for the Analysis of Flood Risk Assessment Based on Artificial Neural Network

2011 ◽  
pp. 262-266 ◽  
Author(s):  
Qiong Li
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Artificial Neural Network ◽  
Flood Risk ◽  
New Method ◽  
Flood Risk Assessment ◽  
Artificial Neural

scholarly journals A new method for dynamic modelling of bread dough kneading based on artificial neural network

Food Control ◽  
2012 ◽  
Vol 26 (2) ◽  
pp. 512-524 ◽  
Author(s):  
Bouchra Lamrini ◽  
Guy Della Valle ◽  
Ioan Cristian Trelea ◽  
Nathalie Perrot ◽  
Gilles Trystram
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dynamic Modelling ◽  
New Method ◽  
Bread Dough ◽  
Artificial Neural

An Artificial Neural Network and Bayesian Network model for liquidity risk assessment in banking

2018 ◽  
Vol 275 ◽  
pp. 2525-2554 ◽  
Author(s):  
Madjid Tavana ◽  
Amir-Reza Abtahi ◽  
Debora Di Caprio ◽  
Maryam Poortarigh
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Artificial Neural Network ◽  
Bayesian Network ◽  
Network Model ◽  
Liquidity Risk ◽  
Bayesian Network Model ◽  
Artificial Neural

scholarly journals Using Artificial Neural Network and Fuzzy Inference System Based Prediction to Improve Failure Mode and Effects Analysis: A Case Study of the Busbars Production

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1444
Author(s):  
Saeed Na’amnh ◽  
Muath Bani Salim ◽  
István Husti ◽  
Miklós Daróczi
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Classical Method ◽  
Fuzzy Model ◽  
Ann Model ◽  
Inference System ◽  
Artificial Neural

Nowadays, Busbars have been extensively used in electrical vehicle industry. Therefore, improving the risk assessment for the production could help to screen the associated failure and take necessary actions to minimize the risk. In this research, a fuzzy inference system (FIS) and artificial neural network (ANN) were used to avoid the shortcomings of the classical method by creating new models for risk assessment with higher accuracy. A dataset includes 58 samples are used to create the models. Mamdani fuzzy model and ANN model were developed using MATLAB software. The results showed that the proposed models give a higher level of accuracy compared to the classical method. Furthermore, a fuzzy model reveals that it is more precise and reliable than the ANN and classical models, especially in case of decision making.

A New Method Based on Artificial Neural Network Techniques for Determining the Fraction of Binaries in Star Clusters

1996 ◽  
Vol 462 ◽  
pp. 221 ◽  
Author(s):  
Miquel Serra-Ricart ◽  
Antonio Aparicio ◽  
Lluis Garrido ◽  
Vicens Gaitan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Star Clusters ◽  
New Method ◽  
Artificial Neural
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