El objetivo de esta Tesis Doctoral ha sido básicamente el de realizar un estudio comparativo sobre los diferentes métodos existentes para resolver diversos aspectos referentes al diseño de la arquitectura de red neuronal Multilayer Feedforward, en problemas de clasificación de redes neuronales.Los aspectos de diseño de la arquitectura de red neuronal estudiados han sido: codificación de entradas desconocidas, selección de la información de entrada a la red, selección del número de unidades ocultas, influencia en la capacidad de generalización del número de capas ocultas e inicialización de pesos de la red.Para cada uno de los aspectos se ha realizado un estudio comparativo de los diferentes métodos existentes para resolver dicho problema. Como resultado recomendamos finalmente el uso de los mejores métodos a la hora de realizar una aplicación concreta. / The objective of this Doctoral Thesis was to carry a comparative study on several existent methods in order to solve different aspects of the design of Multilayer Feedforward architecture, in neural networks classification problems.The aspects of design studied were: handling unknown input information, input selection, selection of the number of hidden units, influence in the generalization capability of the number of hidden layers and weight initialization.For each one of these aspects, we carried out a comparative study of several existent methods in order to solve the problem. We recommend the use of the best methods in order to develop a concrete application.

This thesis is focused on the analysis and development of Ensembles of Neural Networks. An ensemble is a system in which a set of heterogeneous Artificial Neural Networks are generated in order to outperform the Single network based classifiers. However, this proposed thesis differs from others related to ensembles of neural networks [1, 2, 3, 4, 5, 6, 7] since it is organized as follows. In this thesis, firstly, an ensemble methods comparison has been introduced in order to provide a rank-based list of the best ensemble methods existing in the bibliography. This comparison has been split into two researches which represents two chapters of the thesis. Moreover, there is another important step related to the ensembles of neural networks which is how to combine the information provided by the neural networks in the ensemble. In the bibliography, there are some alternatives to apply in order to get an accurate combination of the information provided by the heterogeneous set of networks. For this reason, a combiner comparison has also been introduced in this thesis. Furthermore, Ensembles of Neural Networks is only a kind of Multiple Classifier System based on neural networks. However, there are other alternatives to generate MCS based on neural networks which are quite different to Ensembles. The most important systems are Stacked Generalization and Mixture of Experts. These two systems will be also analysed in this thesis and new alternatives are proposed. One of the results of the comparative research developed is a deep understanding of the field of ensembles. So new ensemble methods and combiners can be designed after analyzing the results provided by the research performed. Concretely, two new ensemble methods, a new ensemble methodology called Cross-Validated Boosting and two reordering algorithms are proposed in this thesis. The best overall results are obtained by the ensemble methods proposed. Finally, all the experiments done have been carried out on a common experimental setup. The experiments have been repeated ten times on nineteen different datasets from the UCI repository in order to validate the results. Moreover, the procedure applied to set up specific parameters is quite similar in all the experiments performed. It is important to conclude by remarking that the main contributions are: 1) An experimental setup to prepare the experiments which can be applied for further comparisons. 2) A guide to select the most appropriate methods to build and combine ensembles and multiple classifiers systems. 3) New methods proposed to build ensembles and other multiple classifier systems.

In the presented master paper: Analysis of car number plate recognition, optical character recognition (OCR), OCR software, OCR devices and systems are reviewed. Image processing operators and artificial neural networks are presented. Analysis and application of image processing operators for detection of number plate is done. Experimental results of estimation of Kohonen and multilayer feedforward artificial neural network learning parameters are presented. Number plate recognition is performed by the use of multilayer feedforward artificial neural network. Model of number plate recognition system is created. Number plate recognition software works in Microsoft© Windows™ operating system. Software is written with C++ language. Experimental results of system model operation are presented.