Flight Dynamics Identification of a Helicopter in Hovering Based on Flight Data

Ren, Qianqian; Feng, Yi

doi:10.1109/aici.2010.346

Cited by 5 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5 In recent years, some studies have been carried out on aircraft dynamics system modeling and identification by NNs, whose favorable results are indicative of the potential of this method in such applications. [6][7][8][9][10][11] Regarding the capabilities of NNs, they can be a reliable candidate to replace the existing methods of aircraft modeling and simulation. 12 Although NNs have been successfully applied for aircraft dynamics modeling, obtaining learning algorithm and network architecture for a suitable generalization and having a proper validation for new input, still remains a dilemma.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary optimizing approach to neural network architecture for improving identification and modeling of aircraft nonlinear dynamics

Roudbari

Saghafi

2013

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

In this paper, modified genetic algorithm has been used as a simultaneous optimizer of recurrent neural network to improve identification and modeling of aircraft nonlinear dynamics. Weighted connections, network architecture, and learning rules are features that play important roles in the quality of neural networks training and their generalizability in order to model nonlinear systems. Therefore, the main focus of this paper is to apply appropriate evolutionary methods in order to simultaneously optimize the parameters of neural networks for the improvement identification and modeling of aircraft nonlinear dynamics. To validate this study, the results have been compared with the recorded data from a fourth generation highly maneuverable fighter aircraft flight test. Furthermore, having been compared to normal genetic algorithm, the results of the present study have showed significant improvement of the neural networks generalization which leads to better identification and modeling of aircraft nonlinear dynamics.

show abstract