VLSI Artificial Neural Networks Engineering:
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...
Gespeichert in:
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1994
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| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (XV, 329 p) |
| ISBN: | 9781461527664 |
| DOI: | 10.1007/978-1-4615-2766-4 |
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| 520 | |a 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 | ||
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Datensatz im Suchindex
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| id | DE-604.BV045186994 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:58Z |
| institution | BVB |
| isbn | 9781461527664 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030576170 |
| oclc_num | 1053814778 |
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| physical | 1 Online-Ressource (XV, 329 p) |
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| publishDate | 1994 |
| publishDateSearch | 1994 |
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| publisher | Springer US |
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| spelling | VLSI Artificial Neural Networks Engineering edited by Mohamed I. Elmasry Boston, MA Springer US 1994 1 Online-Ressource (XV, 329 p) txt rdacontent c rdamedia cr rdacarrier 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 Engineering Circuits and Systems Electrical Engineering Statistical Physics, Dynamical Systems and Complexity Statistical physics Dynamical systems Electrical engineering Electronic circuits Elmasry, Mohamed I. edt Erscheint auch als Druck-Ausgabe 9781461361947 https://doi.org/10.1007/978-1-4615-2766-4 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | VLSI Artificial Neural Networks Engineering Engineering Circuits and Systems Electrical Engineering Statistical Physics, Dynamical Systems and Complexity Statistical physics Dynamical systems Electrical engineering Electronic circuits |
| title | VLSI Artificial Neural Networks Engineering |
| title_auth | VLSI Artificial Neural Networks Engineering |
| title_exact_search | VLSI Artificial Neural Networks Engineering |
| title_full | VLSI Artificial Neural Networks Engineering edited by Mohamed I. Elmasry |
| title_fullStr | VLSI Artificial Neural Networks Engineering edited by Mohamed I. Elmasry |
| title_full_unstemmed | VLSI Artificial Neural Networks Engineering edited by Mohamed I. Elmasry |
| title_short | VLSI Artificial Neural Networks Engineering |
| title_sort | vlsi artificial neural networks engineering |
| topic | Engineering Circuits and Systems Electrical Engineering Statistical Physics, Dynamical Systems and Complexity Statistical physics Dynamical systems Electrical engineering Electronic circuits |
| topic_facet | Engineering Circuits and Systems Electrical Engineering Statistical Physics, Dynamical Systems and Complexity Statistical physics Dynamical systems Electrical engineering Electronic circuits |
| url | https://doi.org/10.1007/978-1-4615-2766-4 |
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