Aimed at senior undergraduate or first-year graduate courses in neural networks and neurocomputing, this work presents neural network theory for diverse applications in a unified way, where the structures of artificial neural networks are characterized by distinguished classes of graphs.
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McGraw-Hill Education - Europe
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Für höhere Schule und Studium
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ISBN-13
978-0-07-114064-5 (9780071140645)
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Schweitzer Klassifikation
Part 1 Fundamentals: basics of neuroscience and artificial neuron models; graphs; algorithms. Part 2 Feedforward networks: perceptrons and LMS algorithm; complexity of learning using feedforward networks; adaptive structure networks. Part 3 Recurrent networks: symmetric and asymmetric recurrent network; competitive learning and self-organizing networks. Part 4 Applications of neural networks: neural networks approach to solving hard problems.
