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Titlebook: VLSI Artificial Neural Networks Engineering; Mohamed I. Elmasry Book 1994 Springer Science+Business Media New York 1994 CMOS.VLSI.analog.a

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书目名称VLSI Artificial Neural Networks Engineering
编辑Mohamed I. Elmasry
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图书封面Titlebook: VLSI Artificial Neural Networks Engineering;  Mohamed I. Elmasry Book 1994 Springer Science+Business Media New York 1994 CMOS.VLSI.analog.a
描述Engineers have long been fascinated by how efficient and how fast biological neural networks are capable of performing such complex tasks as recognition. Such networks are capable of recognizing input data from any of the five senses with the necessary accuracy and speed to allow living creatures to survive. Machines which perform such complex tasks as recognition, with similar ac­ curacy and speed, were difficult to implement until the technological advances of VLSI circuits and systems in the late 1980‘s. Since then, the field of VLSI Artificial Neural Networks (ANNs) have witnessed an exponential growth and a new engineering discipline was born. Today, many engineering curriculums have included a course or more on the subject at the graduate or senior under­ graduate levels. Since the pioneering book by Carver Mead; "Analog VLSI and Neural Sys­ tems", Addison-Wesley, 1989; there were a number of excellent text and ref­ erence books on the subject, each dealing with one or two topics. This book attempts to present an integrated approach of a single research team to VLSI ANNs Engineering.
出版日期Book 1994
关键词CMOS; VLSI; analog; automation; complexity; design automation; model; network; networks; neural networks; patt
版次1
doihttps://doi.org/10.1007/978-1-4615-2766-4
isbn_softcover978-1-4613-6194-7
isbn_ebook978-1-4615-2766-4
copyrightSpringer Science+Business Media New York 1994
The information of publication is updating

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A Compact VLSI Implementation of Neural Networks,al and electronic circuitry, it is possible to emulate many neural functions in VLSI [.]. Analog remains potentially advantageous over digital for low-precision processing [.]. Fortunately, ANNs are more tolerant of low-precision components than conventional computation systems. In particular, ANNs
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Minimum Complexity Neural Networks for Classification,character recognition, speech-to-text and text-to-speech systems, and many more. A Pattern is a set of features arranged together in a vector form, capturing information about the sources generating them. For example, a pattern in a medical diagnosis system would contain features such as patient’s w
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Book 1994on. Such networks are capable of recognizing input data from any of the five senses with the necessary accuracy and speed to allow living creatures to survive. Machines which perform such complex tasks as recognition, with similar ac­ curacy and speed, were difficult to implement until the technolog
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