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.

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

THE PROBLEM OF MODELLING A TRUNK AIR ROUTE NETWORK

Aviation ◽  
2013 ◽  
Vol 17 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Tatiana Tseytlina ◽  
Victor Balashov ◽  
Andrey Smirnov
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Neural Network ◽  
Development Environment ◽  
Route Network ◽  
Information Models ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Neural Network Toolbox

In this work we developed a fuzzy neural network-based model of the conditions for the existence of air routes, i.e. the rules underlying the emergence, existence and elimination of air routes (direct links between cities). The model belongs to the class of information models: the existence or non-existence of an air route is considered dependent on a complex of parameters. These parameters characterise the transport link, as well as the generational and target capabilities of the connected cities. The model was constructed using genetic algorithm techniques and self-organising Kohonen maps (implemented by software features of the STATISTICA package), as well as software tools of the Fuzzy Logic Toolbox and the Neural Network Toolbox of the MatLab development environment. The model is used to forecast the development of the topology of the network. The forecast is a necessary component of long-term forecasts of demand in the aircraft market.

scholarly journals A Compound Controller of an Aerial Manipulator Based on Maxout Fuzzy Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xinchen Qi ◽  
Jianwei Wu ◽  
Jiansheng Pan
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Activation Function ◽  
Attitude Error ◽  
The Neural Network ◽  
Aerial Manipulator ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
The Stability

The aerial manipulator is a complex system with high coupling and instability. The motion of the robotic arm will affect the self-stabilizing accuracy of the unmanned aerial vehicles (UAVs). To enhance the stability of the aerial manipulator, a composite controller combining conventional proportion integration differentiation (PID) control, fuzzy theory, and neural network algorithm is proposed. By blurring the attitude error signal of UAV as the input of the neural network, the anti-interference ability and stability of UAV is improved. At the same time, a neural network model identifier based on Maxout activation function is built to realize accurate recognition of the controlled model. The simulation results show that, compared with the conventional PID controller, the composite controller combined with fuzzy neural network can improve the anti-interference ability and stability of UAV greatly.

Condenser Fault Diagnosis Based on FNN and Data Fusion

2010 ◽  
Vol 44-47 ◽  
pp. 3762-3766 ◽  
Author(s):  
Fei Xia ◽  
Hao Zhang ◽  
Dao Gang Peng ◽  
Hui Li ◽  
Yi Kang Su
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Fuzzy Neural Network ◽  
Membership Functions ◽  
Fusion Technology ◽  
New Approach ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
General Method

In order to improve the fault diagnosis result of the condenser, one new approach based on the fuzzy neural network and data fusion is proposed in this paper. Firstly, the data from the various sensors can be processed through the specific membership functions. With the fault symptoms and fault types of condenser, the fuzzy neural network is constructed for the primary fault diagnosis. Some likelihood of the neural network outputs is too close to make the correct decision of fault diagnosis. The problem can be solved by the data fusion technology. This method was successfully adopted in the application of condenser fault diagnosis. Compared with the general method of FNN, this approach can enhance the accuracy in the domain of fault diagnosis, especially for reducing the uncertainty in the fault diagnosis.

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