Fast diagonal recurrent neural networks for indentification and control of non-linear systems

Youlal, H.; Kada, A.; Ramzi, Mustapha; El-Ismaili, L.

doi:10.1049/cp:19940249

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Identi¢cation Of Coe⁄cients Using a Specialized Neural Netwo1

Drnn Weight Training1

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Cited by 5 publications

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“…The neural network is very useful to deal with nonlinear dynamical systems with unknown nonlinearity (Youlal et al 1994). In this research, a neural networks system has been introduced and applied to identification of the parameters of the two models to best fit experimental data.…”

Section: Identi¢cation Of Coe⁄cients Using a Specialized Neural Netwomentioning

confidence: 99%

Modeling Ship Equations of Roll Motion Using Neural Networks

Xing

McCue

2010

Naval Engineers Journal

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A neural network‐based approach is applied to fit ship rolling models to experimental data and the initial reporting of this methodology is presented in the work of Xing and McCue. Two multivariable nonlinear models are used to describe the nonlinear forced roll motion of a ship at sea. One, a more traditional model, is based on ordinary differential equations, and the other is based on fractional differential equations (FDEs), which introduced a fractional derivative term to present added hydrodynamic inertia and traditional damping terms. The neural network method is tested using experimental data. The statistical analysis of 20 cases results showed that the FDEs appeared to better approximate the physics of the system.

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Section: Identi¢cation Of Coe⁄cients Using a Specialized Neural Netwomentioning

confidence: 99%