scholarly journals Semiphysical Modelling of the Nonlinear Dynamics of a Surface Craft with LS-SVM

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
David Moreno-Salinas ◽  
Dictino Chaos ◽  
Eva Besada-Portas ◽  
José Antonio López-Orozco ◽  
Jesús M. de la Cruz ◽  
...  
Keyword(s):  
Predictive Ability ◽  
Experimental Tests ◽  
Support Vector ◽  
Nonlinear Mathematical Model ◽  
Prediction Ability ◽  
Experimental Systems ◽  
Research Fields ◽  
Vector Machines ◽  
Rudder Angle ◽  
Real Experimental Data

One of the most important problems in many research fields is the development of reliable mathematical models with good predictive ability to simulate experimental systems accurately. Moreover, in some of these fields, as marine systems, these models play a key role due to the changing environmental conditions and the complexity and high cost of the infrastructure needed to carry out experimental tests. In this paper, a semiphysical modelling technique based on least-squares support vector machines (LS-SVM) is proposed to determine a nonlinear mathematical model of a surface craft. The speed and steering equations of the nonlinear model of Blanke are determined analysing the rudder angle, surge and sway speeds, and yaw rate from real experimental data measured from a zig-zag manoeuvre made by a scale ship. The predictive ability of the model is tested with different manoeuvring experimental tests to show the good performance and prediction ability of the model computed.

scholarly journals A Novel Homogenous Hybridization Scheme for Performance Improvement of Support Vector Machines Regression in Reservoir Characterization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Kabiru O. Akande ◽  
Taoreed O. Owolabi ◽  
Sunday O. Olatunji ◽  
AbdulAzeez Abdulraheem
Keyword(s):  
Support Vector Machines ◽  
Reservoir Characterization ◽  
Petroleum Industry ◽  
Predictive Ability ◽  
Extraction Process ◽  
Superior Performance ◽  
Support Vector ◽  
Prediction Ability ◽  
Intelligent Algorithms ◽  
Vector Machines

Hybrid computational intelligence is defined as a combination of multiple intelligent algorithms such that the resulting model has superior performance to the individual algorithms. Therefore, the importance of fusing two or more intelligent algorithms to achieve better performance cannot be overemphasized. In this work, a novel homogenous hybridization scheme is proposed for the improvement of the generalization and predictive ability of support vector machines regression (SVR). The proposed and developed hybrid SVR (HSVR) works by considering the initial SVR prediction as a feature extraction process and then employs the SVR output, which is the extracted feature, as its sole descriptor. The developed hybrid model is applied to the prediction of reservoir permeability and the predicted permeability is compared to core permeability which is regarded as standard in petroleum industry. The results show that the proposed hybrid scheme (HSVR) performed better than the existing SVR in both generalization and prediction ability. The outcome of this research will assist petroleum engineers to effectively predict permeability of carbonate reservoirs with higher degree of accuracy and will invariably lead to better reservoir. Furthermore, the encouraging performance of this hybrid will serve as impetus for further exploring homogenous hybrid system.

scholarly journals Identification of a Surface Marine Vessel Using LS-SVM

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
David Moreno-Salinas ◽  
Dictino Chaos ◽  
Jesús Manuel de la Cruz ◽  
Joaquín Aranda
Keyword(s):  
Experimental Data ◽  
Support Vector Machines ◽  
System Identification ◽  
Mathematical Models ◽  
Nonlinear System Identification ◽  
Experimental Tests ◽  
Support Vector ◽  
Experimental Systems ◽  
Research Areas ◽  
Vector Machines

The availability of adequate system models to reproduce, as faithfully as possible, the actual behaviour of the experimental systems is of key importance. In marine systems, the changing environmental conditions and the complexity of the infrastructure needed to carry out experimental tests call for mathematical models for accurate simulations. There exist a wide number of techniques to define mathematical models from experimental data. Support Vector Machines (SVMs) have shown a great performance in pattern recognition and classification research areas having an inherent potential ability for linear and nonlinear system identification. In this paper, this ability is demonstrated through the identification of the Nomoto second-order ship model with real experimental data obtained from a zig-zag manoeuvre made by a scale ship. The mathematical model of the ship is identified using Least Squares Support Vector Machines (LS-SVMs) for regression by analysing the rudder angle, surge and sway speed, and yaw rate. The coefficients of the Nomoto model are obtained with a linear kernel function. The model obtained is validated through experimental tests that illustrate the potential of SVM for system identification.

scholarly journals Applying Least Squares Support Vector Machines to Mean-Variance Portfolio Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jian Wang ◽  
Junseok Kim
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Portfolio Management ◽  
Stock Exchange ◽  
Efficient Frontier ◽  
Support Vector ◽  
Research Fields ◽  
Vector Machines ◽  
Mean Variance Portfolio ◽  
Mean Variance

Portfolio selection problem introduced by Markowitz has been one of the most important research fields in modern finance. In this paper, we propose a model (least squares support vector machines (LSSVM)-mean-variance) for the portfolio management based on LSSVM. To verify the reliability of LSSVM-mean-variance model, we conduct an empirical research and design an algorithm to illustrate the performance of the model by using the historical data from Shanghai stock exchange. The numerical results show that the proposed model is useful when compared with the traditional Markowitz model. Comparing the efficient frontier and total wealth of both models, our model can provide a more measurable standard of judgment when investors do their investment.

Parametric Identification of Ship Maneuvering Models by Using Support Vector Machines

2009 ◽  
Vol 53 (01) ◽  
pp. 19-30 ◽  
Author(s):  
W. L. Luo ◽  
Z. J. Zou
Keyword(s):  
Support Vector Machines ◽  
Predictive Ability ◽  
Parametric Identification ◽  
Inner Product ◽  
Support Vector ◽  
Identification Algorithm ◽  
Hydrodynamic Coefficients ◽  
Ship Maneuvering ◽  
Vector Machines ◽  
Maneuvering Motion

System identification combined with free-running model tests or full-scale trials is one of the effective methods to determine the hydrodynamic coefficients in the mathematical models of ship maneuvering motion. By analyzing the available data, including rudder angle, surge speed, sway speed, yaw rate, and so forth, a method based on support vector machines (SVM) to estimate the hydrodynamic coefficients is proposed for conventional surface ships. The coefficients are contained in the expansion of the inner product of a linear kernel function. Predictions of maneuvering motion are conducted by using the parameters identified. The results of identification and simulation demonstrate the validity of the identification algorithm proposed. The simultaneous drift and multicollinearity are diminished by introducing an additional ramp signal to the training samples. Comparison between the simulated and predicted motion variables from different maneuvers shows good predictive ability of the trained SVM.

scholarly journals Systematic Literature Review pada Analisis Prediktif dengan IoT: Tren Riset, Metode, dan Arsitektur

2020 ◽  
Vol 3 (1) ◽  
pp. 43-53
Author(s):  
Fahrur Rozi
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Smart Home ◽  
Research Question ◽  
Support Vector ◽  
Research Fields ◽  
Vector Machines ◽  
Artificial Neural Network Ann

Nowadays IoT researches on intelligent service systems is becoming a trend. IoT produces a variety of data from sensors or smart phones. Data generated from IoT can be more useful and can be followed up if data analysis is carried out. Predictive analytic with IoT is part of data analysis that aims to predict something solution. This analysis utilization produces innovative applications in various fields with diverse predictive analytic methods or techniques. This study uses Systematic Literature Review (SLR) to understand about research trends, methods and architecture used in predictive analytic with IoT. So the first step is to determine the research question (RQ) and then search is carried out on several literature published in popular journal databases namely IEEE Xplore, Scopus and ACM from 2015 - 2019. As a result of a review of thirty (30) selected articles, there are several research fields which are trends, namely Transportation, Agriculture, Health, Industry, Smart Home, and Environment. The most studied fields are agriculture. Predictive analytic with IoT use varied method according to the conditions of data used. There are five most widely used methods, namely Bayesian Network (BN), Artificial Neural Network (ANN), Recurrent Neural Networks (RNN), Neural Network (NN), and Support Vector Machines (SVM). Some studies also propose architectures that use predictive analytic with IoT.

MONOTONIC SUPPORT VECTOR MACHINES FOR CREDIT RISK RATING

2009 ◽  
Vol 05 (03) ◽  
pp. 557-570 ◽  
Author(s):  
MICHAEL DOUMPOS ◽  
CONSTANTIN ZOPOUNIDIS
Keyword(s):  
Support Vector Machines ◽  
Pattern Classification ◽  
Credit Rating ◽  
Monotone Function ◽  
Predictive Ability ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Probability Of Default ◽  
Modeling Framework ◽  
Vector Machines

Credit rating models are widely used by banking institutions to assess the creditworthiness of credit applicants and to estimate the probability of default. Several pattern classification algorithms are used for the development of such models. In contrast to other pattern classification tasks, however, credit rating models are not only expected to provide accurate predictions, but also to make clear economic sense. Within this context, the estimated probability of default is often required to be a monotone function of the independent variables. Most machine learning techniques do not take this requirement into account. In this paper, monotonicity hints are used to address this issue within the modeling framework of support vector machines (SVM), which have become increasingly popular in this field. Non-linear SVM credit rating models are developed with linear programming, taking into account the monotonicity requirement. The obtained results indicate that the introduction of monotonicity hints improves the predictive ability of the models.

Blind Prediction of Ship Maneuvering by Using Support Vector Machines

2010 ◽  
Author(s):  
Wei-lin Luo ◽  
Zao-jian Zou
Keyword(s):  
Support Vector Machines ◽  
Black Box ◽  
Support Vector ◽  
Nonlinear Functions ◽  
Ship Maneuvering ◽  
Vector Machines ◽  
Turning Test ◽  
Yaw Moment ◽  
Rudder Angle ◽  
Maneuvering Motion

Support Vector Machines (SVM) based system identification is applied to predict ship maneuvering motion. Different from the prediction methods based on the explicit mathematical model of ship maneuvering motion, the black-box model of ship maneuvering motion is constructed and used to predict ship maneuvering motion. With the rudder angle and the variables of maneuvering motion as inputs and the hydrodynamic forces as outputs, the complicated nonlinear functions in the Abkowitz model are identified; and the surge force, sway force and yaw moment are predicted blindly by using the functions identified. Taking turning test as example, with the rudder angle as inputs and the maneuverability parameters of turning circles as outputs, the input-output mappings are identified and the maneuverability parameters such as the advance, the transfer and the tactical diameter are also predicted blindly by using the identified mappings.

scholarly journals QSAR Modeling of Acute Neurotoxicity of Some Organic Solvents with Respect to Rodents

2018 ◽  
Vol 1 (3) ◽  
pp. e00019
Author(s):  
V.Yu. Grigorev ◽  
O.E. Raevskaya ◽  
A.V. Yarkov ◽  
O.A. Raevsky
Keyword(s):  
Linear Regression ◽  
Organic Solvents ◽  
Predictive Ability ◽  
Proton Acceptor ◽  
Support Vector ◽  
Regression Equations ◽  
Qsar Modeling ◽  
Vector Machines ◽  
Acute Neurotoxicity ◽  
Rats And Mice

Using literature data analysis, the regression models of acute sublethal neurotoxicity of 47 organic solvents with respect to rats and mice have been developed. To construct the models, we used linear regression, random forest and support vector machines approaches. The linear regression equations were selected as the best models. They are designed on the basis of four molecular descriptors: polarizability, sum of positive atom charges, sum of proton acceptor descriptors and dipole moment. The developed models have good descriptive and predictive ability and clear physicochemical interpretation.

scholarly journals QuaBingo: A Prediction System for Protein Quaternary Structure Attributes Using Block Composition

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Chi-Hua Tung ◽  
Chi-Wei Chen ◽  
Ren-Chao Guo ◽  
Hui-Fuang Ng ◽  
Yen-Wei Chu
Keyword(s):  
Quaternary Structure ◽  
Predictive Ability ◽  
Support Vector ◽  
Functional Domain ◽  
Prediction System ◽  
Functional Protein ◽  
Domain Composition ◽  
Vector Machines ◽  
Quaternary Structures ◽  
Quaternary Assembly

Background. Quaternary structures of proteins are closely relevant to gene regulation, signal transduction, and many other biological functions of proteins. In the current study, a new method based on protein-conserved motif composition in block format for feature extraction is proposed, which is termed block composition.Results. The protein quaternary assembly states prediction system which combines blocks with functional domain composition, called QuaBingo, is constructed by three layers of classifiers that can categorize quaternary structural attributes of monomer, homooligomer, and heterooligomer. The building of the first layer classifier uses support vector machines (SVM) based on blocks and functional domains of proteins, and the second layer SVM was utilized to process the outputs of the first layer. Finally, the result is determined by the Random Forest of the third layer. We compared the effectiveness of the combination of block composition, functional domain composition, and pseudoamino acid composition of the model. In the 11 kinds of functional protein families, QuaBingo is 23% of Matthews Correlation Coefficient (MCC) higher than the existing prediction system. The results also revealed the biological characterization of the top five block compositions.Conclusions. QuaBingo provides better predictive ability for predicting the quaternary structural attributes of proteins.

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