Article | Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016 | Comparison of Different Models for Residuary Resistance Prediction Linköping University Electronic Press Conference Proceedings
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Title:
Comparison of Different Models for Residuary Resistance Prediction
Author:
Elizabeta Lazarevska: Faculty of Electrical Engineering and Information Technologies - Skopje, University Ss. Cyril and Methodius - Skopje, Macedonia
DOI:
10.3384/ecp17142511
Download:
Full text (pdf)
Year:
2018
Conference:
Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016
Issue:
142
Article no.:
074
Pages:
511-517
No. of pages:
7
Publication type:
Abstract and Fulltext
Published:
2018-12-19
ISBN:
978-91-7685-399-3
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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The paper presents several unconventional models of residuary resistance based on fuzzy logic and neural network techniques. First, two fuzzy models are built based on different hull parameters and different Froude numbers. These models are identified by a modification of Sugeno and Yasukawa identification algorithm. Next, a neuro-fuzzy model of residuary resistance is build, based on statistical learning theory. The model presents a fuzzy inference system of Takagi and Sugeno type that uses an extended relevance vector machine for learning its parameters and number of fuzzy rules. Finally, a neural network approach is applied to build four different models of residuary resistance. Two of the neural models apply classic extreme learning machine, and the other two implement incremental extreme learning machine philosophies. The obtained models are validated for their generalization and approximation performance, and although they all possess excellent approximation capabilities, our neural models based on extreme learning machine have shown the best simulation results.

Keywords: residuary resistance, fuzzy modeling, neuro-fuzzy model, extreme learning machine, random nodes

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Elizabeta Lazarevska
Title:
Comparison of Different Models for Residuary Resistance Prediction
DOI:
http://dx.doi.org/10.3384/ecp17142511
References:
No references available

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Elizabeta Lazarevska
Title:
Comparison of Different Models for Residuary Resistance Prediction
DOI:
https://doi.org10.3384/ecp17142511
Note: the following are taken directly from CrossRef
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Last updated: 2019-10-02