2013
DOI: 10.1177/0954410013514030
|View full text |Cite
|
Sign up to set email alerts
|

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

Abstract: 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 th… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3

Citation Types

0
3
0

Year Published

2016
2016
2024
2024

Publication Types

Select...
4
1

Relationship

1
4

Authors

Journals

citations
Cited by 5 publications
(3 citation statements)
references
References 18 publications
0
3
0
Order By: Relevance
“…Some novel parameter identification approaches have been proposed recently. 1215 Particularly, there is a growing interest for parameter identification via deep learning (DL) methods. 16,17 DL is a representation-learning method which can automatically extract multiple levels features of input data by training and has been successfully applied to many fields.…”
Section: Introductionmentioning
confidence: 99%
“…Some novel parameter identification approaches have been proposed recently. 1215 Particularly, there is a growing interest for parameter identification via deep learning (DL) methods. 16,17 DL is a representation-learning method which can automatically extract multiple levels features of input data by training and has been successfully applied to many fields.…”
Section: Introductionmentioning
confidence: 99%
“…Thus far, numerous system identification algorithms have been applied by researchers. 3–8 The frequency domain analysis, 9,10 fuzzy identification, 1113 the state space identification, 14,15 and the artificial neural networks 1619 are amongst the most renowned methods. Generally, the structures of the identification algorithms are classified as linear or nonlinear.…”
Section: Introductionmentioning
confidence: 99%
“…Generally, the structures of the identification algorithms are classified as linear or nonlinear. Some of these well developed, efficient identification methods have been successfully implemented in aircraft system identification 1620 ; however, the theory behind most of these algorithms deals with linear systems using the well-established techniques of linear algebra and the theory of ordinary differential equations. Over the past few decades, classic linear identification methods have advanced to such an extent that they are now considered standard tools in a variety of engineering fields.…”
Section: Introductionmentioning
confidence: 99%