http://hdl.handle.net/10016/9756 Thu, 20 Jun 2013 08:07:38 GMT 2013-06-20T08:07:38Z http://hdl.handle.net/10016/12504 Title: Automatic design of artificial neural networks to forecast time series Author(s): Peralta, Juan; Gutiérrez, Germán; Sanchis, Araceli Abstract: In this work an approach to design Artificial Neural Networks (ANN) to forecast Time Series is tackled. The approach is an automatic method that is carried out by an Evolutionary Algorithm (as a search algorithm) to design ANN. A key issue for these kinds of approaches is what information is included into the chromosome that represents an ANN There are two principal ideas about this question: first, the chromosome contains information about parameters of the topology, architecture, learning parameters, etc. of the ANN. The results using a parameter Encoding Scheme to design ANN for a Time Series Competition are shown Description: Actas de: III Simposio de Inteligencia Computacional, SICO 2010, Valencia, 8-10 septiembre, 2010 Thu, 31 Dec 2009 23:00:00 GMT http://hdl.handle.net/10016/12504 2009-12-31T23:00:00Z http://hdl.handle.net/10016/12177 Title: Testing feature selection in traffic signs Author(s): Sesmero, M. P.; Alonso, J.M.; Gutiérrez, Germán; Sanchis, Araceli Abstract: Road signs carry essential information for successful driving. Therefore, if we are interested in developing a Driver Support Systems, both, detection and classification of road signs are essential tasks for an autonomous system. However, both tasks are some of the less studied subjects in the field of Intelligent Transport systems. In this research we lay the foundations of a software implementation for a classifier system that will be implemented in hardware and will be able to be used for real-time traffic sign categorization. The selected classification method is a Multilayer Perceptron trained with Back-Propagation algorithm. The reason of this selection is, on one hand, that for certain types of problems, such as object recognition in natural environments, neural network learning methods provide a robust approach. On the other hand, and under certain, limitations related mainly to the number of units, a hardware implementation on FPGA of ANN is possible. Therefore, ANNs are a good method for real-time processing in real-word problems Sun, 31 Dec 2006 23:00:00 GMT http://hdl.handle.net/10016/12177 2006-12-31T23:00:00Z http://hdl.handle.net/10016/12125 Title: Cooperación en sistemas distribuidos de robots reactivos minimizando la cantidad de información comunicada Author(s): Fernández, Fernando; Gutiérrez, Germán; Molina, José M. Abstract: La coordinación emergente pretende obtener comportamientos colaborativos entre diversos agentes sin que eso implique que cada individuo deba tener un conocimiento global del dominio, y sin que ese conocimiento deba estar centralizado. Al no requerir conocimiento global, se minimiza la comunicación entre los agentes de forma que cada uno de ellos puede comportarse de forma reactiva y totalmente autónoma. En este trabajo se presenta una primera aproximación a este modelo de coordinación aplicado al dominio de la RoboCup. Description: Actas de: Simposio Español de Infomática Distribuida (SEID 2000), Ourense, 25-27 de septiembre de 2000 Fri, 31 Dec 1999 23:00:00 GMT http://hdl.handle.net/10016/12125 1999-12-31T23:00:00Z http://hdl.handle.net/10016/11716 Title: MMRF for proteome annotation applied to human protein disease prediction Author(s): García-Jiménez, Beatriz; Ledezma, Agapito; Sanchis, Araceli Abstract: Biological processes where every gene and protein participates is an essential knowledge for designing disease treatments. Nowadays, these annotations are still unknown for many genes and proteins. Since making annotations from in-vivo experiments is costly, computational predictors are needed for different kinds of annotation such as metabolic pathway, interaction network, protein family, tissue, disease and so on. Biological data has an intrinsic relational structure, including genes and proteins, which can be grouped by many criteria. This hinders the possibility of finding good hypotheses when attribute-value representation is used. Hence, we propose the generic Modular Multi-Relational Framework (MMRF) to predict different kinds of gene and protein annotation using Relational Data Mining (RDM). The specific MMRF application to annotate human protein with diseases verifies that group knowledge (mainly protein-protein interaction pairs) improves the prediction, particularly doubling the area under the precision-recall curve Description: Proceedings of: 20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010 Fri, 31 Dec 2010 23:00:00 GMT http://hdl.handle.net/10016/11716 2010-12-31T23:00:00Z