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Öğe Implementing soft computing techniques to solve economic dispatch problem in power systems(PERGAMON-ELSEVIER SCIENCE LTD, 2008) Altun, H.; Yalcinoz, T.Soft computing is the state-of-the-art approach to artificial intelligence and it has showed an excellent performance in solving the combined optimization problems. In this paper, issues related to the implementation of the soft computing techniques are highlighted for a successful application to solve economic dispatch (ED) problem, which is a constrained optimization problem in power systems. First of all, a survey covering the basics of the techniques is presented and then implementation of the techniques in the ED problem is discussed. The soft computing techniques, namely tabu search (TS), genetic algorithm (GA), Hopfield neural network (HNN) and multi-layered perceptron (MLP) are applied to solve the ED problem. The techniques are tested on power systems consisting of 6 and 20 generating units and the results are compared to highlight the performance of the soft computing techniques. Future directions and open-ended problems in implementation of soft computing techniques for constrained optimization problems in power system are indicated. Suggestions are presented to improve soft computing techniques. (C) 2007 Elsevier Ltd. All rights reserved.Öğe Investigation of flow resistance in smooth open channels using artificial neural networks(ELSEVIER SCI LTD, 2008) Bilgil, A.; Altun, H.An accurate prediction of the friction coefficient is very important in hydraulic engineering since it directly affects the design of water structures, the calculation of velocity distribution, and an accurate determination of energy losses. However, conventional approaches that are profoundly based on empirical methods lack in providing high accuracy for the prediction of the friction coefficient. Consequently, new and accurate techniques are still highly demanded. This study introduces an efficient approach to estimate the friction coefficient via an artificial neural network, which is a promising computational tool in civil engineering. The estimated value of the friction coefficient is used in Manning Equation to predict the open channel flows in order to carry out a comparison between the proposed neural networks based approach and the conventional ones. Results show that the proposed approach is in good agreement with the experimental results when compared to the conventional ones. (C) 2008 Elsevier Ltd. All rights reserved.Öğe New feature selection frameworks in emotion recognition to evaluate the informative power of speech related features(IEEE, 2007) Altun, H.; Shawe-Taylor, J.; Polat, G.In this paper, we propose two new frameworks, so as to boost the feature selection algorithms in a way that the selected features will be more informative in terms of class-separability. In the first framework, features that are more informative in discriminating an emotional class from the rest of the classes are favoured for selection by the feature selection algorithms. In the second framework features that more informative in terms of separating an emotional class from another one are favoured for selection. Then, final feature subsets are constructed from the subsets of selected features using intersection and unification operators. It will be shown that the proposed frameworks fulfill the objectives by considerably reducing average cross-validation error.Öğe Treatment of multi-dimensional data to enhance neural network estimators in regression problems(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Altun, H.; Bilgil, A.; Fidan, B. C.This paper proposes and explains a data treatment technique to improve the accuracy of a neural network estimator in regression problems, where multi-dimensional input data set is highly skewed and non-normally distributed. The proposed treatment modifies the distribution characteristics of the data set. The prediction of the suspended sediment, which is an important problem in river engineering applications, will be considered as a case study. Conventional approaches lack in providing high accuracy due to the inherently employed simplicity in order to obtain empirical formulae. On the other hand, artificial neural networks are able to model the non-linear characteristics of the mechanism of the sediment transport and have a growing body of applications in diverse applications in civil engineering. It will be shown that a significant enhancement and superior score in accuracy, compared with the classical approaches, are obtainable when the proposed treatment is employed. The proposed technique is an extension to the understanding of the practical aspects of neural computing applications. Therefore the outcome of the present study is important as it is applicable to any scenario where neural network approaches are involved. (C) 2006 Elsevier Ltd. All rights reserved.Öğe Treatment of skewed multi-dimensional training data to facilitate the task of engineering neural models(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Altun, H.; Bilgil, A.; Fidan, B. C.Successful application of neural network models relies heavily on problem-dependent internal parameters. As the theory does not facilitate the choice of the optimal parameters of neural models, these can solely be obtained through a tedious trial-and-error process. The process requires performing multiple training simulations with various network parameters, until satisfactory performance criteria of a neural model are met. In literature, it has been shown that neural models are not consistently good in prediction under highly skewed data. Consequently, the cost of engineering neural models rises in such circumstance to seek for appropriate internal parameters. In this paper the aim is to show that a recently proposed treatment of highly skewed data eases the task of practitioners in engineering neural network models to meet satisfactory performance criteria. As the applications of neural models grows dramatically in diverse engineering domains, the understanding of the treatment show indispensable practical values. (c) 2006 Elsevier Ltd. All rights reserved.
