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Efficient aerodynamic design through evolutionary programming and support vector regression algorithms

EXPERT SYSTEMS WITH APPLICATIONS 2012年第12期39卷 10700-10708页

作者： Andres, E. Salcedo-Sanz, S. Monge, F. Perez-Bellido, A. M. Univ Alcala Dept Signal Theory & Commun Madrid 28871 Spain Natl Inst Aerosp Technol INTA Fluid Dynam Branch Torrejon De Ardoz Spain

The shortening of the design cycle and the increase of the performance are nowadays the main challenges in aerodynamic design. The use of evolutionary algorithms (EAs) seems to be appropriate in a preliminary phase, due to their ability to broadly explore the design space and obtain global optima. Evolutionary algorithms have been hybridized with metamodels (or surrogate models) in several works published in the last years, in order to substitute expensive computational fluid dynamics (CFD) simulations. In this paper, an advanced approach for the aerodynamic optimization of aeronautical wing profiles is proposed, consisting of an evolutionary programming algorithm hybridized with a support vector regression algorithm (SVMr) as a metamodel. Specific issues as precision, dataset training size and feasibility of the complete approach are discussed and the potential of global optimization methods (enhanced by metamodels) to achieve innovative shapes that would not be achieved with traditional methods is assessed. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Aerodynamic design Airfoil optimization Evolutionary optimization support vector regression algorithms Aerodynamic coefficient prediction Computational fluid dynamics

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Short term wind speed prediction based on evolutionary support vector regression algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第4期38卷 4052-4057页

作者： Salcedo-Sanz, Sancho Ortiz-Garcia, Emilio G. Perez-Bellido, Angel M. Portilla-Figueras, Antonio Prieto, Luis Univ Alcala de Henares Dept Signal Theory & Commun Madrid 28871 Spain Iberdrola Renovables Energy Resource Dept Madrid Spain

Hyper-parameters estimation in regression support vector Machines (SVMr) is one of the main problems in the application of this type of algorithms to learning problems. This is a hot topic in which very recent approaches have shown very good results in different applications in fields such as bio-medicine, manufacturing, control, etc. Different evolutionary approaches have been tested to be hybridized with SVMr, though the most used are evolutionary approaches for continuous problems, such as evolutionary strategies or particle swarm optimization algorithms. In this paper we discuss the application of two different evolutionary computation techniques to tackle the hyper-parameters estimation problem in SVMrs. Specifically we test an Evolutionary Programming algorithm (EP) and a Particle Swarm Optimization approach (PSO). We focus the paper on the discussion of the application of the complete evolutionary-SVMr algorithm to a real problem of wind speed prediction in wind turbines of a Spanish wind farm. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： support vector regression algorithms Evolutionary algorithms Hyper-parameters estimation Short-term wind speed forecasting

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Prediction of hourly O₃ concentrations using support vector regression algorithms

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ATMOSPHERIC ENVIRONMENT 2010年第35期44卷 4481-4488页

作者： Ortiz-Garcia, E. G. Salcedo-Sanz, S. Perez-Bellido, A. M. Portilla-Figueras, J. A. Prieto, L. Univ Alcala Dept Signal Theory & Commun Madrid 28871 Spain Univ Complutense Madrid Dept Phys Earth Astron & Astrophys 2 E-28040 Madrid Spain

In this paper we present an application of the support vector regression algorithm (SVMr) to the prediction of hourly ozone values in Madrid urban area. In order to improve the training capacity of SVMrs, we have used a recently proposed approach, based on reductions of the SVMr hyper-parameters search space. Using the modified SVMr, we study different influences which may modify the ozone prediction, such as previous ozone measurements in a given station, measurements in neighbors stations, and the influence of meteorologic variables. We use statistical tests to verify the significance of incorporating different variables into the SVMr. A comparison with the results obtained using a neural network (multi-layer perceptron) is also carried out. This study has been carried out in 5 different stations of the air pollution monitoring network of Madrid, so the conclusions raised are backed by real data. The final result of the work is a robust and powerful software for tropospheric ozone prediction in Madrid. Also, the prediction tool based on SVMr is flexible enough to incorporate any other prediction variable, such as city models, or traffic patters, which may improve the prediction obtained with the SVMr. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： O-3 concentration prediction support vector regression algorithms Air quality

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Improving the training time of support vector regression algorithms through novel hyper-parameters search space reductions

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NEUROCOMPUTING 2009年第16-18期72卷 3683-3691页

作者： Ortiz-Garcia, Emilio G. Salcedo-Sanz, Sancho Perez-Bellido, Angel M. Portilla-Figueras, Jose A. Univ Alcala de Henares Dept Signal Theory & Commun Grp Heurist Modernos Optimizac & Diseno Redes GHE Madrid 28871 Spain

The selection of hyper-parameters in support vector machines (SVM) is a key point in the training process of these models when applied to regression problems. Unfortunately, an exact method to obtain the optimal set of SVM hyper-parameters is unknown, and search algorithms are usually applied to obtain the best possible set of hyper-parameters. In general these search algorithms are implemented as grid searches, which are time consuming, so the computational cost of the SVM training process increases considerably. This paper presents a novel study of the effect of including reductions in the range of SVM hyper-parameters, in order to reduce the SVM training time, but with the minimum possible impact in its performance. The paper presents reduction in parameter C, by considering its relation with the rest of SVM hyper-parameters (gamma and epsilon), through an approximation of the SVM model. On the other hand, we use some characteristics of the Gaussian kernel function and a previous result in the literature to obtain novel bounds for gamma and epsilon hyper-parameters. The search space reductions proposed are evaluated in different regression problems from UCI and StatLib databases. All the experiments carried out applying the popular LIBSVM solver have shown that our approach reduces the SVM training time, maintaining the SVM performance similar to when the complete range in SVM parameters is considered. (C) 2009 Elsevier B.V. All rights reserved.

关键词： support vector regression algorithms SVM hyper-parameters Training time Search space reductions

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Escherichia coli bacteria detection by using graphene-based biosensor

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IET NANOBIOTECHNOLOGY 2015年第5期9卷 273-279页

作者： Akbari, Elnaz Buntat, Zolkafle Afroozeh, Abdolkarim Zeinalinezhad, Alireza Nikoukar, Ali Univ Teknol Malaysia Inst High Voltage & High Current Fac Elect Engn Johor Baharu 81310 Malaysia Islamic Azad Univ Jahrom Branch Young Researchers & Elite Club Jahrom Iran Dept Gen Fars Prov Educ Shiraz Iran Islamic Azad Univ Anar Branch Dept Chem Anar Iran Univ Teknol Malaysia Fac Comp Johor Baharu 81310 Malaysia

Graphene is an allotrope of carbon with two-dimensional (2D) monolayer honeycombs. A larger detection area and higher sensitivity can be provided by graphene-based nanosenor because of its 2D structure. In addition, owing to its special characteristics, including electrical, optical and physical properties, graphene is known as a more suitable candidate compared to other materials used in the sensor application. A novel model employing a field-effect transistor structure using graphene is proposed and the current-voltage (I-V) characteristics of graphene are employed to model the sensing mechanism. This biosensor can detect Escherichia coli (E. coli) bacteria, providing high levels of sensitivity. It is observed that the graphene device experiences a drastic increase in conductance when exposed to E. coli bacteria at 0-10(5) cfu/ml concentration. The simple, fast response and high sensitivity of this nanoelectronic biosensor make it a suitable device in screening and functional studies of antibacterial drugs and an ideal high-throughput platform which can detect any pathogenic bacteria. Artificial neural network and support vector regression algorithms have also been used to provide other models for the I-V characteristic. A satisfactory agreement has been presented by comparison between the proposed models with the experimental data.

关键词： biosensors nanosensors microorganisms drugs neural nets support vector machines regression analysis medical computing 2D monolayer honeycombs 2D nanosensor structure antibacterial drug screening artificial neural network bacteria detection biosensor sensitivity level biosensor-based bacterial detection biosensor-detected E coli bacteria biosensor-detected Escherichia coli carbon allotrope E coli detection Escherichia coli detection field-effect transistor structure graphene current-voltage characteristics graphene device conductance graphene I-V characteristics graphene-based biosensor graphene-based nanosenor ideal high-throughput bacterial detection platform nanoelectronic biosensor sensitivity nanosensor electrical properties nanosensor optical properties nanosensor physical properties nanosensor sensitivity level nanosensor-provided detection area nanosensor-provided sensitivity pathogenic bacteria-detecting biosensor sensing mechanism modeling special nanosensor characteristics support vector regression algorithms two-dimensional monolayer honeycombs

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Accurate local very short-term temperature prediction based on synoptic situation support vector regression banks

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ATMOSPHERIC RESEARCH 2012年 107卷 1-8页

作者： Ortiz-Garcia, E. G. Salcedo-Sanz, S. Casanova-Mateo, C. Paniagua-Tineo, A. Portilla-Figueras, J. A. Univ Alcala Dept Signal Theory & Commun Madrid 28871 Spain Univ Valladolid Dept Appl Phys E-47002 Valladolid Spain

In this paper we present a novel system for addressing problems of local very short term (up to a time prediction horizon of 6 h) temperature prediction based on support vector regression algorithms (SVMr). Specifically, we construct SVMr banks based on the synoptic situation for each prediction period, incorporated by means of the well-known Hess-Brezowsky classification (HBC). We show how this SVMr bank structure obtains very good results in a real problem of short-term temperature prediction at Barcelona-El Prat International Airport (Spain), obtaining an average RMSE of 1.34 degrees C in 6 hour horizon prediction. Comparison with alternative neural techniques have been carried out in order to show the effectiveness of the proposed technique, and how the inclusion of the HBC classification is also able to improve the performance of these alternative neural algorithms in the problem. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Short-term temperature prediction support vector regression algorithms Synoptic grouping-based ensembles

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New validation methods for improving standard and multi-parametric support vector regression training time

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第9期39卷 8220-8227页

作者： Gascon-Moreno, J. Salcedo-Sanz, S. Ortiz-Garcia, E. G. Acevedo-Rodriguez, J. Portilla-Figueras, Jose A. Univ Alcala Dept Signal Theory & Commun Madrid 28871 Spain

The selection of hyper-parameters in support vector regression algorithms (SVMr) is an essential process in the training of these learning machines. Unfortunately, there is not an exact method to obtain the optimal values of SVMr hyper-parameters. Therefore, it is necessary to use a search algorithm and sometimes a validation method in order to find the best combination of hyper-parameters. The problem is that the SVMr training time can be huge in large training databases if standard search algorithms and validation methods (such as grid search and K-fold cross validation), are used. In this paper we propose two novel validation methods which reduce the SVMr training time, maintaining the accuracy of the final machine. We show the good performance of both methods in the standard SVMr with 3 hyper-parameters (where the hyper-parameters search is usually carried out by means of a grid search) and also in the extension to multi-parametric kernels, where meta-heuristic approaches such as evolutionary algorithms must be used to look for the best set of SVMr hyper-parameters. In all cases the new validation methods have provided very good results in terms of training time, without affecting the final SVMr accuracy. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： support vector regression algorithms SVMr hyper-parameters Training time Validation methods

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Prediction of daily maximum temperature using a support vector regression algorithm

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RENEWABLE ENERGY 2011年第11期36卷 3054-3060页

作者： Paniagua-Tineo, A. Salcedo-Sanz, S. Casanova-Mateo, C. Ortiz-Garcia, E. G. Cony, M. A. Hernandez-Martin, E. Univ Alcala Dept Signal Theory & Commun Madrid 28871 Spain Ctr Energy Environm & Technol Res CIEMAT Dept Renewable Energy Madrid Spain Univ Complutense Madrid Dept Phys Earth Astron & Astrophys 2 E-28040 Madrid Spain

Daily maximum temperature can be used a good indicator of peak energy consumption, since it can be used to predict the massive use of heating or air conditioning systems. Thus, the prediction of daily maximum temperature is an important problem with interesting applications in the energy field, since it has been proven that electricity demand depends much on weather conditions. This paper presents a novel methodology for daily maximum temperature prediction, based on a support vector regression approach. The paper is focused on different measuring stations in Europe, from which different meteorological variables have been obtained, including temperature, precipitation, relative humidity and air pressure. Two more variables are also included, specifically synoptic situation of the day and monthly cycle. Using this pool of prediction variables, it is shown that the SVMr algorithm is able to give an accurate prediction of the maximum temperature 24 h later. In the paper SVMr technique applied is fully described, including some bounds on the machine hyper-parameters in order to speed up the SVMr training process. The performance of the SVMr has been compared to that of different neural networks in the literature: a Multi-layer perceptron and an Extreme Learning Machine. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Daily maximum temperature prediction support vector regression algorithms Neural networks

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Short-term wind speed prediction in wind farms based on banks of support vector machines

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WIND ENERGY 2011年第2期14卷 193-207页

作者： Ortiz-Garcia, Emilio G. Salcedo-Sanz, Sancho Perez-Bellido, Angel M. Gascon-Moreno, Jorge Portilla-Figueras, Jose A. Prieto, Luis Univ Alcala Dept Signal Theory & Commun Grp Heurist Modernos Optimizac & Diseno Redes GHE Madrid 28871 Spain Iberdrola Renovables Energy Resource Dept Madrid 28033 Spain

Wind speed prediction is a key point in the management of wind farms because it is directly related to the power produced by each of a farm's turbines. Wind speed prediction is usually one of the most important tasks in wind farming, and companies that manage these farms invest large amounts of money to improve their prediction systems. In this paper, we propose an improvement to an existing wind speed prediction system, using banks of regression support vector Machines (SVMr) for a final regression step in the system. Several novel SVMr structures are proposed in this paper to manage the diversity in input data arising from the use of different global forecasting models and several parameterizations of a mesoscale model, included in the basic version of the prediction system. We show that the system implementing SVMr banks outperforms the basic system without taking into account diversity in the input data. It also performs better than a similar system using banks of multi-layer perceptrons. All the tests are carried out using real data from several wind turbines on a wind farm in southeast Spain. Copyright (C) 2010 John Wiley & Sons, Ltd.

关键词： wind speed forecast support vector regression algorithms banks of regression SVM (SVMr)

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