Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications (The Springer International Series in Engineering and Computer Science, 209)

Karayiannis, Nicolaos, Venetsanopoulos, Anastasios N.

ISBN 10: 0792392973 / ISBN 13: 9780792392972

Editore: Kluwer, 1992

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Codice articolo ZB1291985

Dati bibliografici

• Titolo: Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications (The Springer International Series in Engineering and Computer Science, 209)
• Editore: Kluwer
• Data di pubblicazione: 1992
• Lingua: Inglese
• ISBN 10: 0792392973
• ISBN 13: 9780792392972
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

1.1 Overview We are living in a decade recently declared as the "Decade of the Brain". Neuroscientists may soon manage to work out a functional map of the brain, thanks to technologies that open windows on the mind. With the average human brain consisting of 15 billion neurons, roughly equal to the number of stars in our milky way, each receiving signals through as many as 10,000 synapses, it is quite a view. "The brain is the last and greatest biological frontier", says James Weston codiscoverer of DNA, considered to be the most complex piece of biological machinery on earth. After many years of research by neuroanatomists and neurophys­ iologists, the overall organization of the brain is well understood, but many of its detailed neural mechanisms remain to be decoded. In order to understand the functioning of the brain, neurobiologists have taken a bottom-up approach of studying the stimulus-response characteristics of single neurons and networks of neurons, while psy­ chologists have taken a top-down approach of studying brain func­ tions from the cognitive and behavioral level. While these two ap­ proaches are gradually converging, it is generally accepted that it may take another fifty years before we achieve a solid microscopic, intermediate, and macroscopic understanding of brain.

Contenuti

1. Introduction. 2. Neural Network Architectures and Learning Schemes. 3. ELEANNE: Efficient LEarning Algorithms for Neural NEtworks. 4. Fast Learning Algorithms for Neural Networks. 5. ALADIN: Algorithms for Learning and Architecture DetermINation. 6. Performance Evaluation of Single-Layered Neural Networks. 7. High-Order Neural Networks and Networks with Composite Key Patterns. 8. Applications of Neural Networks: A Case Study. 9. Applications of Neural Networks: A Review. 10. Future Trends and Directions. References. Subject Index. Author Index.