Predictions of oil/chemical tanker main design parameters using computational intelligence techniques

This paper aims to model the change in market share of 30 domestic and foreign banks, which have been operating between the years 1990 and 2009 in Turkey by taking into consideration 20 financial ratios of those banks. Due to the fragile structure of the banking sector in Turkey, this study plays an important role for determining the changes in market share of banks and taking the necessary measures promptly. For this reason, computational intelligence methods have been used in the study. According to the research results, it is seen that it was not able to properly anticipate the data for the banking sector in the periods of financial crises (2000-2001 and 2008-2009). However, it is seen that, Simple Linear Regression is distinguished as a good algorithm among the computational intelligence algorithms for all periods between the years 1990 and 2009.

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“…where is the number of parameters in the model, RSS is the residual of sum of the squares, and is the number of observations [35].…”

Section: Linear Regression (Lr)mentioning

confidence: 99%

“…The model having lowest squared error is selected as the final model among each parameter model [35]. [42].…”

Section: Simple Linear Regression (Slr)mentioning

confidence: 99%

Analysis of Changes in Market Shares of Commercial Banks Operating in Turkey Using Computational Intelligence Algorithms

Amasyalı

Demi̇rhan

Bal

2014

Advances in Artificial Intelligence

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“…Computational intelligence 7 is a new category of algorithms, including evolutionary computing, fuzzy computing, rough set, artificial neural network (ANN) and granular computing, swarm computing, etc. The wide applications of these intelligence algorithms have proven that they are very useful in practice for solving actual problems, especially for those problems of obtaining hard deterministic solutions.…”

Section: Computational Intelligencementioning

confidence: 99%

A novel fault analysis and diagnosis method based on combining computational intelligence methods

Yang

Liu

Zhao

et al. 2012

Proceedings of the Institution of Mechanical Engineers, Part E:

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In order to improve the correctness and efficiency of fault diagnosis, a novel hybrid intelligence method based on integrating rough set, genetic algorithms, and radial basic function neural network (RGRN) was proposed for motor fault diagnosis in the complicated CNC system in this article. In the proposed RGRN method, combination and condition supplement algorithm was used to deal with the incomplete fault data and the original data were discretized using genetic algorithms to construct a decision table. Rough set theory as a new mathematical tool was used to eliminate the redundant and irrelevant attributes in order to obtain the minimum rule set for reducing the number of input nodes of the radial basic function neural network. Genetic algorithms were directly used to optimize the structure and weights of radial basic function neural network to establish an optimized radial basic function neural network (GRN) model; then, the minimum rule set was inputted into the GRN model in order to obtain the optimized RGRN model. Finally, the completed fault symptom information was inputted into the RGRN model to obtain the fault diagnosis results. The robustness of the RGRN method was tested. Simulating experiments on motor fault diagnosis in the complicated CNC system show the RGRN method not only improves the global optimization performance and quickens the convergence speed, but also obtains the robust solution with a better quality.

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“…Ekinci et al [3], through different computational intelligence techniques, determined principal parameters of contemporary oil/chemical tankers, also including main engine power. These authors took only into account length between perpendiculars instead of using a deadweight to engine power assessment.…”

Section: Introductionmentioning

confidence: 99%

Regression Formulas for The Estimation of Engine Total Power for Tankers, Container Ships and Bulk Carriers on The Basis of Cargo Capacity and Design Speed

Cepowski

2019

Polish Maritime Research

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This article presents regression formulas for the preliminary design of tankers, bulk carriers and container vessels, based on the data of ships built from 2000 to 2018. The formulas could have practical application for the estimation of total engine power by using ship’s deadweight or TEU capacity and speed. The regressions presented in this article are based on the most recent data and were developed for individual sub-types of tankers, bulk carriers and container ships. The presented regressions comply with trends found in the literature and offer greater accuracy for characteristics of new-built ships.

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Predictions of oil/chemical tanker main design parameters using computational intelligence techniques

Cited by 32 publications

References 17 publications

Analysis of Changes in Market Shares of Commercial Banks Operating in Turkey Using Computational Intelligence Algorithms

Analysis of Changes in Market Shares of Commercial Banks Operating in Turkey Using Computational Intelligence Algorithms

A novel fault analysis and diagnosis method based on combining computational intelligence methods

Regression Formulas for The Estimation of Engine Total Power for Tankers, Container Ships and Bulk Carriers on The Basis of Cargo Capacity and Design Speed

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