Title :
Application of RBF Network in System Identification for Flight Control Systems
Author :
Yijun, Huang ; Wu, Niu
Author_Institution :
First Aeronaut. Coll. of Air Force, Xinyang, China
Abstract :
The flight control system (FCS) is a complex nonlinear multi-input and multi-output) system, it is very difficult to identify the model of this system. The nonlinear relation of I/O data can be expressed by artificial neural network (ANN). The ANN can fit the any function accurately by studying. In this paper, the FCS of a type of fighter is identified by radial basis function (RBF) network. The training algorithm of the RBF network is improved by a grouping optimizing method and a new training algorithm. Simulation results about application of this network with new algorithm were given. The results show that the complex FCS can be identified by artificial neural network accurately.
Keywords :
MIMO systems; aerospace control; large-scale systems; neurocontrollers; nonlinear control systems; optimisation; radial basis function networks; I/O data; RBF network; artificial neural network; complex nonlinear multiinput and multioutput system; flight control system; grouping optimizing method; radial basis function network; system identification; training algorithm; Aerospace control; Artificial neural networks; Estimation; Radial basis function networks; Simulation; System identification; Training; Radial basis functions; flight control system; neural network; system identification;
Conference_Titel :
Information Technology and Applications (IFITA), 2010 International Forum on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-7621-3
Electronic_ISBN :
978-1-4244-7622-0
DOI :
10.1109/IFITA.2010.264
