The Barnacle Goose, a distinctive, handsome black-and-white bird, gets its name from a mediaeval myth that the birds hatched from barnacles – how else to explain their sudden appearance each autumn in northern Britain? We now know, of course, that the birds migrate from Arctic Russia, Norway and Svalbard to winter throughout northern Europe. This book represents a culmination of more than 25 years of Barnacle Goose research. It represents the story of one of Europe's most celebrated long-term behavioral studies, detailing the lives of these social and sociable birds. Chapters include sections on pair formation and bonding, family and population dynamics, brood parasitism, food and feeding, size and shape in different populations, life cycle, survivorship, dispersal, migration, and conservation, with particular regard to climate change. It is a rigorous and thorough examination of the lives of these birds, in fine Poyser tradition.
The Barnacle Goose, a distinctive, handsome black-and-white bird, gets its name from a mediaeval myth that the birds hatched from barnacles – how else to explain their sudden appearance each autumn in northern Britain? We now know, of course, that the birds migrate from Arctic Russia, Norway and Svalbard to winter throughout northern Europe. This book represents a culmination of more than 25 years of Barnacle Goose research. It represents the story of one of Europe's most celebrated long-term behavioral studies, detailing the lives of these social and sociable birds. Chapters include sections on pair formation and bonding, family and population dynamics, brood parasitism, food and feeding, size and shape in different populations, life cycle, survivorship, dispersal, migration, and conservation, with particular regard to climate change. It is a rigorous and thorough examination of the lives of these birds, in fine Poyser tradition.
Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation presents a novel method for the mathematical analysis of neural networks that learn according to the back-propagation algorithm. The book also discusses some other recent alternative algorithms for hardware implemented perception-like neural networks. The method permits a simple analysis of the learning behaviour of neural networks, allowing specifications for their building blocks to be readily obtained. Starting with the derivation of a specification and ending with its hardware implementation, analog hard-wired, feed-forward neural networks with on-chip back-propagation learning are designed in their entirety. On-chip learning is necessary in circumstances where fixed weight configurations cannot be used. It is also useful for the elimination of most mis-matches and parameter tolerances that occur in hard-wired neural network chips. Fully analog neural networks have several advantages over other implementations: low chip area, low power consumption, and high speed operation. Feed-Forward Neural Networks is an excellent source of reference and may be used as a text for advanced courses.
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.