Neural network modeling of the nonlinear dynamic structural offshore system with hysteresis

Autor:	D. L. Kaiser, D. M. Rocha, Nelson F. F. Ebecken, L. P. Calôba
Rok vydání:	2008
Předmět:	Nonlinear system Nonlinear autoregressive exogenous model Recurrent neural network Control theory Dynamic loading Computer science Time delay neural network Bending moment Fault (power engineering) Representation (mathematics)
Zdroj:	Data Mining IX.
ISSN:	1743-3517 1746-4463
DOI:	10.2495/data080021
Popis:	This paper proposes an empirical modeling of the system formed by the riserplatform connection, in deep water. This connection has the objective of minimizing the acting bending moment, possesses high complexity and highcriticity due to economic and environmental consequences from its fault. The main element in the joint is made of elastomeric material, which reveals nonlinear hysteresis. In addition, this whole connection system presents nonlinearities due to the action of dynamic loading and large motions. TDNN and Recurrent Neural Networks (RNN) are being investigated since they possess the ability to model nonlinear hysteretic behaviors and also dynamic systems. Simulation results have confirmed that RNN is the one that presents the best representation of the system studied. Emphasis shall be given to the additional difficulties, which arise from the utilization of real data in the modeling process for this system.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9d4986d5417a63763f4be88523658f48 https://doi.org/10.2495/data080021 Zobrazit plný text záznamu