IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T08:54:07ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2011-12-06T13:27:59Z2011-12-06T13:27:59Zhttp://eprints.imtlucca.it/id/eprint/1039This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/10392011-12-06T13:27:59ZDevelopment of nonlinear quantitative structure-activity relationships using RBF networks and evolutionary computingQuantitative Structure Activity Relationships (QSARs) are mathematical models that correlate structural or property descriptions of compounds (hydrophobicity, topology, electronic properties etc.) with activities, such as chemical measurements and biological assays. In this paper we propose a modeling methodology suitable for QSAR studies which selects the proper descriptors based on evolutionary computing and finally produces Radial Basis Function (RBF) neural network models. The method is successfully applied to the benchmark Selwood data set.Panagiotis Patrinospanagiotis.patrinos@imtlucca.itAlex AlexandridisAntreas AfantitisHaralambos SarimveisOlga Igglesi-Markopoulou2011-12-06T11:36:57Z2011-12-06T11:36:57Zhttp://eprints.imtlucca.it/id/eprint/1037This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/10372011-12-06T11:36:57ZNeural network model-based paper machine marginal cost curvesIn this paper we present a methodology for developing paper machine marginal cost curves, which include variable material and energy costs, fixed costs and overhead costs. The methodology is based on the radial basis function (RBF) neural network architecture and takes into account the complex interactions between the different mill departments. The outcome of the proposed method is the calculation of marginal costs for different paper machine production rates and different grades. The resulting cost curves can be used to take optimal decisions regarding paper machine loadings and optimize production allocation.Panagiotis Patrinospanagiotis.patrinos@imtlucca.itHaralambos SarimveisTh RetsinaS.R RutherfordAlex Alexandridis2011-12-06T11:03:54Z2011-12-06T11:03:54Zhttp://eprints.imtlucca.it/id/eprint/1036This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/10362011-12-06T11:03:54ZOptimal production scheduling for dairy industriesIn this work, a complete two-level framework for use in food and in particular dairy industries is proposed. The specific characteristics of the dairy industry have been taken into consideration, in terms of the behavior of food sales over time and the special requirements in the production phase. At the scheduling level, an MILP (Mixed Integer Linear Programming) model of the system was developed, using a continuous representation of time.Philip DoganisHaralambos SarimveisAlex AlexandridisPanagiotis Patrinospanagiotis.patrinos@imtlucca.it2011-12-05T11:40:26Z2011-12-05T11:40:26Zhttp://eprints.imtlucca.it/id/eprint/1027This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/10272011-12-05T11:40:26ZA two-stage evolutionary algorithm for variable selection in the development of RBF neural network modelsIn many modeling problems that are based on input–output data, information about a plethora of variables is available. In these cases, the proper selection of explanatory variables is very critical for the success of the produced model, since it eliminates noisy variables and possible correlations, reduces the size of the model and accomplishes more accurate predictions. Many variable selection procedures have been proposed in the literature, but most of them consider only linear models. In this work, we present a novel methodology for variable selection in nonlinear modeling, which combines the advantages of several artificial intelligence technologies. More specifically, the Radial Basis Function (RBF) neural network architecture serves as the nonlinear modeling tool, by exploiting the simplicity of its topology and the fast fuzzy means training algorithm. The proper variables are selected in two stages using a multi-objective optimization approach: in the first stage, a specially designed genetic algorithm minimizes the prediction error over a monitoring data set, while in the second stage a simulated annealing technique aims at the reduction of the number of explanatory variables. The efficiency of the proposed method is illustrated through its application to a number of benchmark problems.Alex AlexandridisPanagiotis Patrinospanagiotis.patrinos@imtlucca.itHaralambos SarimveisGeorge Tsekouras2011-12-05T11:15:09Z2011-12-05T11:15:09Zhttp://eprints.imtlucca.it/id/eprint/1026This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/10262011-12-05T11:15:09ZTime series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computingDue to the strong competition that exists today, most manufacturing organizations are in a continuous effort for increasing their profits and reducing their costs. Accurate sales forecasting is certainly an inexpensive way to meet the aforementioned goals, since this leads to improved customer service, reduced lost sales and product returns and more efficient production planning. Especially for the food industry, successful sales forecasting systems can be very beneficial, due to the short shelf-life of many food products and the importance of the product quality which is closely related to human health. In this paper we present a complete framework that can be used for developing nonlinear time series sales forecasting models. The method is a combination of two artificial intelligence technologies, namely the radial basis function (RBF) neural network architecture and a specially designed genetic algorithm (GA). The methodology is applied successfully to sales data of fresh milk provided by a major manufacturing company of dairy products.Philip DoganisAlex AlexandridisPanagiotis Patrinospanagiotis.patrinos@imtlucca.itHaralambos Sarimveis2011-12-05T10:30:11Z2011-12-05T10:30:11Zhttp://eprints.imtlucca.it/id/eprint/1024This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/10242011-12-05T10:30:11ZVariable selection in nonlinear modeling based on RBF networks and evolutionary computationIn this paper a novel variable selection method based on Radial Basis Function (RBF) neural networks and genetic algorithms is presented. The fuzzy means algorithm is utilized as the training method for the RBF networks, due to its inherent speed, the deterministic approach of selecting the hidden node centers and the fact that it involves only a single tuning parameter. The trade-off between the accuracy and parsimony of the produced model is handled by using Final Prediction Error criterion, based on the RBF training and validation errors, as a fitness function of the proposed genetic algorithm. The tuning parameter required by the fuzzy means algorithm is treated as a free variable by the genetic algorithm. The proposed method was tested in benchmark data sets stemming from the scientific communities of time-series prediction and medicinal chemistry and produced promising results.Panagiotis Patrinospanagiotis.patrinos@imtlucca.itAlex AlexandridisKonstantinos NinosHaralambos Sarimveis