System identification for nonlinear maneuvering of large tankers using artificial neural network

Rajesh, G.; Bhattacharyya, S. K.

doi:10.1016/j.apor.2008.10.003

Cited by 79 publications

(27 citation statements)

References 11 publications

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“…They consist of an interconnected group of artificial neuron and process information using a connectionist approach to computation. Neural networks have been proven effective in modeling of complex systems and have a wide range of applications in many areas [1][2][3][4][5] . Once a relationship model between the design parameters and the performance parameters is specified using neural networks, the analysis process of flight performance will become easier.…”

Section: Introductionmentioning

confidence: 99%

Analysis of flight performance using neural networks

Xie

Zheng

Tang

et al. 2012

2012 8th International Conference on Natural Computation

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The analysis of flight performance is a necessary process in system concept design for aircrafts. A key problem of the analysis is to determine the relationship between the design parameters and the performance parameters, which generally relies on a lot of time-consuming simulations and even expensive experiments. In order to improve analysis efficiency and save money, the neural network modeling based on uniform design is employed to determine the relationship models. A relative fewer input-output data are required to training the models. By the models the influence characteristics of the design parameters on the performance can be analyzed rapidly without extensive simulations and experiments. The approach is tested taking the low performance Common Aero Vehicle (CAV-L) as an example. The results show that neural networks are effective in the analysis of flight performance for aircrafts.

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Section: Introductionmentioning

confidence: 99%

Analysis of flight performance using neural networks

Xie

Zheng

Tang

et al. 2012

2012 8th International Conference on Natural Computation

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show abstract

“…As a matter that NN is accomplished in nonlinear regression, it is commonly adopted for black-box modeling of ship maneuvering motion [12][13][14][15][16][17], whereas it is rarely taken into consideration for parametric identification. In contrast, SVM not only can be used for black-box modeling [18], but also adept at approximation of the hydrodynamic derivatives in the mathematical model via the switch of kernel function, i.e., commonly linear kernel function for parametric identification while radius basis function kernel function for nonlinear regression.…”

Section: Introductionmentioning

confidence: 99%

Parametric estimation of ship maneuvering motion with integral sample structure for identification

Cao

Zhuang

et al. 2015

Applied Ocean Research

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“…The second way is where artificial intelligence techniques are employed to obtain an implicit model (or so-called a black-box model) that describes the input-output mapping characteristics of ship dynamics. Examples are the uses of artificial neural networks (ANN) [3,4] and support vector machines (SVM) [5,6].…”

Section: Introductionmentioning

confidence: 99%

Parameter Identifiability of Ship Manoeuvring Modeling Using System Identification

Luo

2016

Mathematical Problems in Engineering

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To improve the feasibility of system identification in the prediction of ship manoeuvrability, several measures are presented to deal with the parameter identifiability in the parametric modeling of ship manoeuvring motion based on system identification. Drift of nonlinear hydrodynamic coefficients is explained from the point of view of regression analysis. To diminish the multicollinearity in a complicated manoeuvring model, difference method and additional signal method are employed to reconstruct the samples. Moreover, the structure of manoeuvring model is simplified based on correlation analysis. Manoeuvring simulation is performed to demonstrate the validity of the measures proposed.

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System identification for nonlinear maneuvering of large tankers using artificial neural network

Cited by 79 publications

References 11 publications

Analysis of flight performance using neural networks

Analysis of flight performance using neural networks

Parametric estimation of ship maneuvering motion with integral sample structure for identification

Parameter Identifiability of Ship Manoeuvring Modeling Using System Identification

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