Phd thesis on neural networks

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Learning algorithms for neural networks - CaltechTHESIS

Atiya, Amir Learning algorithms for neural networks. This thesis /comment-rediger-un-plan-de-dissertation-philo.html mainly with neural networks development of new learning algorithms and the study of the dynamics of phd thesis thesis networks. We develop a method for training feedback neural networks. Appropriate stability conditions are derived, and learning is performed by the gradient descent technique.

Theory and applications of artificial neural networks

We develop a new associative source model using Hopfield's phd thesis on neural networks feedback network. We demonstrate some of the phd thesis on neural networks limitations of the Hopfield network, and develop alternative architectures and an algorithm for designing the associative memory. We propose a new unsupervised learning method for phd thesis networks. The method is based on applying repeatedly the gradient ascent technique on a defined criterion /higher-education-for-and-against-essay.html. We study some of the neural networks aspects of Hopfield networks.

Learning algorithms for neural networks

New stability results are derived. Oscillations and synchronizations in several architectures are studied, and related to recent findings in biology. The problem of recording the outputs of real neural networks is considered. A new method for the detection and the phd thesis on neural phd thesis on neural networks of the recorded neural signals is proposed.

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Theory and applications of artificial neural networks - Durham e-Theses

Learning algorithms for neural networks networks. Citation Atiya, Amir Learning algorithms for neural networks.

Abstract This phd thesis on neural networks deals mainly with the development of new learning algorithms and the study of the dynamics of neural networks. More information and software credits. Learning algorithms for neural networks Citation Atiya, Amir Learning /how-to-make-an-history-essay.html for neural networks.

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