Sage Journals: Discover world-class research

Abstract

An improved time-delay wavelet neural network (WNN) is proposed to represent the complex nonlinear and time-varying dynamics of ship motion based on sensitivity analysis approach. To improve the generalization performance of WNN, inputs of the wavelet network are selected based on their relative contribution to the overall output. To evaluate the contribution of inputs in the WNN, an index is proposed referred to as relative contribution rate (RCR). The resulted network is utilized as an online ship motion predictor. Based on the predictor, a predictive PID controller is presented and implemented in a ship course-following control. Simulation of online predictive ship course-following control was conducted and results demonstrate the feasibility and efficiency of the WNN predictor and the WNN-based predictive control strategy.

Keywords

Wavelet neural network ship motion control predictive control sensitivity analysis

Get full access to this article

View all access options for this article.

References

Jia

X.L.

and Yang

Y.S.

, Mathematic Model of Ship Motion. Dalian Maritime University Press, Dalian, 1998.

Xiu

Z.H.

, Zhang

J.S.

and Zheng

Y.L.

, Bionics in Computational Intelligence, Science Press, Beijing, 2003.

Omerhodzic

, Avdakovic

, Nuhanovic

et al., Energy distribution of EEG signals: EEG signal wavelet-neural network classifier, International Journal of Biological and Life Sciences 6 (2013), 210-215.

Tang

M.M.

and Zhang

J.L.

, A multiple adaptive wavelet recurrent neural network model to analyze crude oil prices, Journal of Economics and Business 64 (2012), 275-286.

Martínez-Morales

J.D.

, Palacios

and Velázquez-Carrillo

G.A.

, Wavelet Neural Networks for Predicting Engine Emissions, Procedia Technology 7 (2013), 328-335.

Sun

, Pei

, Pan

et al., Robust wavelet network control for a class of autonomous vehicles to track environmental contour line, Neurocomputing 74 (2011), 2886-2892.

Alipour

and Hardalac

, Application of genetic algorithms in fuzzy wavelet neural network for fetal electrocardiogram extraction, International Journal of Medical Engineering and Informatics 4 (2012), 176-183.

Lin

C.J.

and Tsai

H.M.

, FPGA implementation of a wavelet neural network with particle swarm optimization learning, Mathematical and Computer Modelling 47 (2008), 982-996.

Alexandridis

A.K.

and Zapranis

A.D.

, Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification, Wiely, USA 2014.

10.

Kamruzzaman

, Shahriar

and Beecham

, Assessment of Short Term Rainfall and Stream Flows in South Australia, Water 6 (2014), 3528-3544.

11.

, Zou

Z.J.

, Yin

J.C.

and Cao

, Parametric identification and sensitivity analysis for autonomous underwater vehicles in diving plane Journal of Hydrodynamics 24 (2012), 744-751.

12.

Zhang

and Benveniste

, Wavelet Networks, IEEE Transctions on Neural Networks 3 (1992), 889-898.

13.

Çoruh

, Geyikçi

, Kılıç

et al., The use of NARX neural network for modeling of adsorption of zinc ions using activated almond shell as a potential biosorbent, Bioresource Technology 151 (2014), 406-410.

14.

Guan

, Luh

P.B.

, Michel

L.D.

et al., Very short-term load forecasting: Wavelet neural networks with data pre-filtering, IEEE Transactions on Power Systems 28 (2013), 30-41.

15.

Saltelli

, Annoni

, Azzini

et al., Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index, Computer Physics Communications 181 (2010), 259-270.

16.

Rhee

K.P.

and Kim

, A new sea trial method for estimating hydrodynamic derivatives, Ship & Ocean Technology 3 (1999), 25-44.

17.

Zeilinger

M.N.

, Raimondo

D.M.

, Domahidi

et al., On real-time robust model predictive control, Automatica 50 (2014), 683-694.

Time-delay wavelet network predictor based on sensitivity analysis with application to predictive ship course control

Abstract

Keywords

Get full access to this article

References