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Descripción
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| Artificial Neural Networks design and training algorithms are based many times on the optimization of an objective error function used to provide an evaluation of the performances of the network. The value of the error depends basically on the weight values of the different connections between the neurons of the network. The learning methods modify and update the different weight values following a strategy that tends to minimize the final error in the network performance. The neural network theory identifies the weight values as a representation of the synaptic weights in the biological neural networks, and their ability to change their values can be interpreted as a kind of artificial plasticity inspired by the demonstrated biological counterpart process. The biological metaplasticity is related to the processes of memory and learning as an inherent property of the biological neuron connections, and consists in the capacity of modifying the learning mechanism using the information present in the network itself. In such a way, Artificial MetaPlasticity (AMP), is interpreted as the ability to change the efficiency of artificial plasticity depending on certain elements used in the training. A very efficient AMP model (as a function of learning time and performance) is the approach that connects metaplasticity and Shannon?s information theory, which establishes that less frequent patterns carry more information than frequent patterns. This model defines AMP as a learning procedure that produces greater modifications in the synaptic weights when less frequent patterns are presented to the network than when frequent patterns are used, as a way of extracting more information from the former than from the latter. In this doctoral thesis the AMP theory is implemented using different Artificial Neural Network (ANN), models and different learning paradigms. The networks are used as classifiers or predictors of synthetic and real data sets in order to be able to compare and evaluate the results obtained with several state of the art methods. The AMP theory is implemented over two general learning methods: | |
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Internacional
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ISBN
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Tipo de Tesis
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Doctoral |
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Calificación
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Aprobado |
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Fecha
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22/06/2018 |