Risk Assessment Model of Ship Integrated Navigation System Based on Fuzzy Artificial Neural Network

2010 ◽  
Author(s):  
Li Hong-bo ◽  
Hao Yan-ling
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Artificial Neural Network ◽  
Navigation System ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Artificial Neural

The Application of Fuzzy Analytic Hierarchy Process Method and Artificial Neural Network Model in the Elevator Risk Assessment

2014 ◽  
Vol 1014 ◽  
pp. 552-555
Author(s):  
Xin Shi Li ◽  
You Cai Xu ◽  
Ran Tao ◽  
Shu Guo ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Artificial Neural Network ◽  
Analytic Hierarchy Process ◽  
Assessment Model ◽  
Fuzzy Analytic Hierarchy Process ◽  
Risk Assessment Model ◽  
Analytic Hierarchy ◽  
Artificial Neural ◽  
Hierarchy Process

The tradition elevator risk assessment model depends on the expert experience, which causes that the assessment process takes a long time. To deal with this problem, this paper proposes a new risk assessment model which is based on fuzzy analytic hierarchy process (F-AHP) and artificial neural network (ANN). This model is applied to the risk-assessment of elevators. The results show that the assessment time is shorter and the accuracy is not lower, in comparison with the traditional model.

Using Artificial Neural Networks to Compensate for the Error in an Integrated Navigation System

2020 ◽  
pp. 4-26
Author(s):  
N. Al Bitar ◽  
A.I. Gavrilov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Navigation System ◽  
Satellite Navigation ◽  
Satellite System ◽  
Integrated Navigation ◽  
Satellite Navigation System ◽  
Integrated Navigation System ◽  
Artificial Neural

The paper presents a new method for improving the accuracy of an integrated navigation system in terms of coordinate and velocity when there is no signal received from the global navigation satellite system. We used artificial neural networks to simulate the error occurring in an integrated navigation system in the absence of the satellite navigation system signal. We propose a method for selecting the inputs for the artificial neural networks based on the mutual information (MI) criterion and lag-space estimation. The artificial neural network employed is a non-linear autoregressive neural network with external inputs. We estimated the efficiency of using our method to solve the problem of compensating for the error in an integrated navigation system in the absence of the satellite navigation system signal

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