Neural network fundamentals with graphs, algorithms, and applications
著者
書誌事項
Neural network fundamentals with graphs, algorithms, and applications
（McGraw-Hill series in electrical and computer engineering, . Communications and signal processing）
McGraw-Hill, c1996
- : pbk
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注記
Includes bibliographical references (p. 447-462) and index
内容説明・目次
内容説明
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.
目次
- 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.
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