Add to ORKGThis thesis presents the implementation of a newly developed minimal Radial Basis Function ( RBF ) neural network using the Minimal Resource Allocation Network ( M-RAN ) sequential learning algorithm for flight control system ap-plications. F-8 and F-16 fighter aircraft models are used in this thesis for the neuro-flight control system application studies. Three flight control architectures using M-RAN have been developed in this study.Master of Engineerin
Aerodynamic parameter estimation involves modelling of force and moment coefficients and computation...
WOS: 000236683500008PubMed ID: 16649568This paper describes the development of a neural network (NN)...
neural control system that can control a simulated aircraft, which ultimately should lead to a reali...
A novel neural network approach based on model-following direct adaptive control system design is pr...
This thesis presents a new sequential learning algorithm for realizing a minimal Radial Basis Funct...
This report presents a neural-aided controller that enhances the fault tolerant high performance fig...
Dynamic Radial Basis Function Neural Network (RBFNN) called Extended Minimum Resource Allocation Neu...
The neural network has the advantages of self-learning, self-adaptation, and fault tolerance. It can...
This paper presents an adaptive backstepping neural controller design for aircraft under control sur...
Flight simulators have been part of aviation history since its beginning. With the development of mo...
This report presents an adaptive back-stepping neural controller for reconfigurable flight control o...
This book presents in detail the newly developed sequential learning algorithm for radial basis func...
In recent years, aircraft accidents have especially increased on Concorde aircrafts. Due to out of c...
For high performance aircrafts, the flight control system needs to be quite effective in both assuri...
International audienceFlight simulators have been part of aviation history since its beginning. With...
Aerodynamic parameter estimation involves modelling of force and moment coefficients and computation...
WOS: 000236683500008PubMed ID: 16649568This paper describes the development of a neural network (NN)...
neural control system that can control a simulated aircraft, which ultimately should lead to a reali...
A novel neural network approach based on model-following direct adaptive control system design is pr...
This thesis presents a new sequential learning algorithm for realizing a minimal Radial Basis Funct...
This report presents a neural-aided controller that enhances the fault tolerant high performance fig...
Dynamic Radial Basis Function Neural Network (RBFNN) called Extended Minimum Resource Allocation Neu...
The neural network has the advantages of self-learning, self-adaptation, and fault tolerance. It can...
This paper presents an adaptive backstepping neural controller design for aircraft under control sur...
Flight simulators have been part of aviation history since its beginning. With the development of mo...
This report presents an adaptive back-stepping neural controller for reconfigurable flight control o...
This book presents in detail the newly developed sequential learning algorithm for radial basis func...
In recent years, aircraft accidents have especially increased on Concorde aircrafts. Due to out of c...
For high performance aircrafts, the flight control system needs to be quite effective in both assuri...
International audienceFlight simulators have been part of aviation history since its beginning. With...
Aerodynamic parameter estimation involves modelling of force and moment coefficients and computation...
WOS: 000236683500008PubMed ID: 16649568This paper describes the development of a neural network (NN)...
neural control system that can control a simulated aircraft, which ultimately should lead to a reali...