A Novel Dual Fuzzy Neural Network to Civil Aviation Aircraft Disturbance Landing Control

2012 ◽  
pp. 131-139 ◽  
Author(s):  
Kaijun Xu
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Civil Aviation ◽  
Fuzzy Neural ◽  
Landing Control

Decisional Autonomy of Approach and Landing Phase for Civil Aviation Aircraft using Dual Fuzzy Neural Network

2012 ◽  
Vol 476-478 ◽  
pp. 936-939 ◽  
Author(s):  
Kai Jun Xu
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Gradient Descent ◽  
Fuzzy Neural Network ◽  
Error Function ◽  
Civil Aviation ◽  
Learning Rates ◽  
The Neural Network ◽  
Automatic Landing ◽  
Fuzzy Neural

This paper presents the dual fuzzy neural network, designed the decisional autonomy flight controller for civil aviation aircraft in approach and landing phase. Real-time learning method was applied to train the neural network using the gradient-descent of an error function to adaptively update weights. Adaptive learning rates were obtained through the analysis of Lyapunov stability to guarantee the convergence of learning. Conventional automatic landing system (ALS) can provide a smooth landing, which is essential to the comfort of passengers. However, these systems work only within a specified operational safety envelope. When the conditions are beyond the envelope, such as turbulence or wind shear, they often cannot be used. The objective of this paper is to investigate the use of dual fuzzy neural network in ALS and to make that system more intelligent.

A Novel Architecture for Civil Aviation Aircraft Intelligent Landing Using Dual Fuzzy Neural Network

2013 ◽  
Vol 313-314 ◽  
pp. 1385-1388
Author(s):  
Kai Jun Xu
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Networks ◽  
Network Control ◽  
Civil Aviation ◽  
Pitch Control ◽  
Roll Control ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Landing Control ◽  
Neural Network Controllers

This paper presents a novel architecture of intelligent landing control of an airplane using dual fuzzy neural networks, including roll control, pitch control and altitude hold control. The neural network control has been implemented in MATLAB and the data for training have been taken from Flight Gear Simulator. The flight performance has been shown in the Flight Gear Simulator. The objective is to improve the performance of conventional landing, roll, pitch and altitude hold controllers. Simulated results show that control for different flight phases is successful and the neural network controllers provide the robustness to system parameter variation.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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