Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
2000
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Multi-Valued and Universal Binary Neurons deals with two new types of neurons: multi-valued neurons and universal binary neurons. These neurons are based on complex number arithmetic and are hence much more powerful than the typical neurons used in artificial neural networks. Therefore, networks with such neurons exhibit a broad functionality. They can not only realise threshold input/output maps but can also implement any arbitrary Boolean function. Two learning methods are presented whereby these networks can be trained easily. The broad applicability of these networks is proven by several case studies in different fields of application: image processing, edge detection, image enhancement, super resolution, pattern recognition, face recognition, and prediction. The book is hence partitioned into three almost equally sized parts: a mathematical study of the unique features of these new neurons, learning of networks of such neurons, and application of such neural networks. Most of this work was developed by the first two authors over a period of more than 10 years and was only available in the Russian literature. With this book we present the first comprehensive treatment of this important class of neural networks in the open Western literature. Multi-Valued and Universal Binary Neurons is intended for anyone with a scholarly interest in neural network theory, applications and learning. It will also be of interest to researchers and practitioners in the fields of image processing, pattern recognition, control and robotics |
| Beschreibung: | 1 Online-Ressource (IV, 276 p) |
| ISBN: | 9781475731156 9781441949783 |
| DOI: | 10.1007/978-1-4757-3115-6 |
Internformat
MARC
| LEADER | 00000nmm a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV042412336 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 150316s2000 |||| o||u| ||||||eng d | ||
| 020 | |a 9781475731156 |c Online |9 978-1-4757-3115-6 | ||
| 020 | |a 9781441949783 |c Print |9 978-1-4419-4978-3 | ||
| 024 | 7 | |a 10.1007/978-1-4757-3115-6 |2 doi | |
| 035 | |a (OCoLC)863936837 | ||
| 035 | |a (DE-599)BVBBV042412336 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-91 |a DE-83 | ||
| 082 | 0 | |a 621 |2 23 | |
| 084 | |a PHY 000 |2 stub | ||
| 100 | 1 | |a Aizenberg, Igor N. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Multi-Valued and Universal Binary Neurons |b Theory, Learning and Applications |c by Igor N. Aizenberg, Naum N. Aizenberg, Joos Vandewalle |
| 264 | 1 | |a Boston, MA |b Springer US |c 2000 | |
| 300 | |a 1 Online-Ressource (IV, 276 p) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 500 | |a Multi-Valued and Universal Binary Neurons deals with two new types of neurons: multi-valued neurons and universal binary neurons. These neurons are based on complex number arithmetic and are hence much more powerful than the typical neurons used in artificial neural networks. Therefore, networks with such neurons exhibit a broad functionality. They can not only realise threshold input/output maps but can also implement any arbitrary Boolean function. Two learning methods are presented whereby these networks can be trained easily. The broad applicability of these networks is proven by several case studies in different fields of application: image processing, edge detection, image enhancement, super resolution, pattern recognition, face recognition, and prediction. The book is hence partitioned into three almost equally sized parts: a mathematical study of the unique features of these new neurons, learning of networks of such neurons, and application of such neural networks. Most of this work was developed by the first two authors over a period of more than 10 years and was only available in the Russian literature. With this book we present the first comprehensive treatment of this important class of neural networks in the open Western literature. Multi-Valued and Universal Binary Neurons is intended for anyone with a scholarly interest in neural network theory, applications and learning. It will also be of interest to researchers and practitioners in the fields of image processing, pattern recognition, control and robotics | ||
| 650 | 4 | |a Physics | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Computer engineering | |
| 650 | 4 | |a Statistical Physics, Dynamical Systems and Complexity | |
| 650 | 4 | |a Artificial Intelligence (incl. Robotics) | |
| 650 | 4 | |a Control, Robotics, Mechatronics | |
| 650 | 4 | |a Electrical Engineering | |
| 650 | 4 | |a Signal, Image and Speech Processing | |
| 650 | 4 | |a Künstliche Intelligenz | |
| 700 | 1 | |a Aizenberg, Naum N. |e Sonstige |4 oth | |
| 700 | 1 | |a Vandewalle, Joos |e Sonstige |4 oth | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-1-4757-3115-6 |x Verlag |3 Volltext |
| 912 | |a ZDB-2-PHA |a ZDB-2-BAE | ||
| 940 | 1 | |q ZDB-2-PHA_Archive | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-027847829 | ||
Datensatz im Suchindex
| _version_ | 1804153074959253504 |
|---|---|
| any_adam_object | |
| author | Aizenberg, Igor N. |
| author_facet | Aizenberg, Igor N. |
| author_role | aut |
| author_sort | Aizenberg, Igor N. |
| author_variant | i n a in ina |
| building | Verbundindex |
| bvnumber | BV042412336 |
| classification_tum | PHY 000 |
| collection | ZDB-2-PHA ZDB-2-BAE |
| ctrlnum | (OCoLC)863936837 (DE-599)BVBBV042412336 |
| dewey-full | 621 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621 |
| dewey-search | 621 |
| dewey-sort | 3621 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Physik |
| doi_str_mv | 10.1007/978-1-4757-3115-6 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03202nmm a2200481zc 4500</leader><controlfield tag="001">BV042412336</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">150316s2000 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781475731156</subfield><subfield code="c">Online</subfield><subfield code="9">978-1-4757-3115-6</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781441949783</subfield><subfield code="c">Print</subfield><subfield code="9">978-1-4419-4978-3</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-1-4757-3115-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)863936837</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV042412336</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield><subfield code="a">DE-83</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">621</subfield><subfield code="2">23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">PHY 000</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Aizenberg, Igor N.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multi-Valued and Universal Binary Neurons</subfield><subfield code="b">Theory, Learning and Applications</subfield><subfield code="c">by Igor N. Aizenberg, Naum N. Aizenberg, Joos Vandewalle</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boston, MA</subfield><subfield code="b">Springer US</subfield><subfield code="c">2000</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (IV, 276 p)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Multi-Valued and Universal Binary Neurons deals with two new types of neurons: multi-valued neurons and universal binary neurons. These neurons are based on complex number arithmetic and are hence much more powerful than the typical neurons used in artificial neural networks. Therefore, networks with such neurons exhibit a broad functionality. They can not only realise threshold input/output maps but can also implement any arbitrary Boolean function. Two learning methods are presented whereby these networks can be trained easily. The broad applicability of these networks is proven by several case studies in different fields of application: image processing, edge detection, image enhancement, super resolution, pattern recognition, face recognition, and prediction. The book is hence partitioned into three almost equally sized parts: a mathematical study of the unique features of these new neurons, learning of networks of such neurons, and application of such neural networks. Most of this work was developed by the first two authors over a period of more than 10 years and was only available in the Russian literature. With this book we present the first comprehensive treatment of this important class of neural networks in the open Western literature. Multi-Valued and Universal Binary Neurons is intended for anyone with a scholarly interest in neural network theory, applications and learning. It will also be of interest to researchers and practitioners in the fields of image processing, pattern recognition, control and robotics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Physics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer engineering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistical Physics, Dynamical Systems and Complexity</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial Intelligence (incl. Robotics)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Control, Robotics, Mechatronics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Electrical Engineering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Signal, Image and Speech Processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Künstliche Intelligenz</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Aizenberg, Naum N.</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Vandewalle, Joos</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/978-1-4757-3115-6</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-PHA</subfield><subfield code="a">ZDB-2-BAE</subfield></datafield><datafield tag="940" ind1="1" ind2=" "><subfield code="q">ZDB-2-PHA_Archive</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-027847829</subfield></datafield></record></collection> |
| id | DE-604.BV042412336 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:20:50Z |
| institution | BVB |
| isbn | 9781475731156 9781441949783 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027847829 |
| oclc_num | 863936837 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM DE-83 |
| owner_facet | DE-91 DE-BY-TUM DE-83 |
| physical | 1 Online-Ressource (IV, 276 p) |
| psigel | ZDB-2-PHA ZDB-2-BAE ZDB-2-PHA_Archive |
| publishDate | 2000 |
| publishDateSearch | 2000 |
| publishDateSort | 2000 |
| publisher | Springer US |
| record_format | marc |
| spelling | Aizenberg, Igor N. Verfasser aut Multi-Valued and Universal Binary Neurons Theory, Learning and Applications by Igor N. Aizenberg, Naum N. Aizenberg, Joos Vandewalle Boston, MA Springer US 2000 1 Online-Ressource (IV, 276 p) txt rdacontent c rdamedia cr rdacarrier Multi-Valued and Universal Binary Neurons deals with two new types of neurons: multi-valued neurons and universal binary neurons. These neurons are based on complex number arithmetic and are hence much more powerful than the typical neurons used in artificial neural networks. Therefore, networks with such neurons exhibit a broad functionality. They can not only realise threshold input/output maps but can also implement any arbitrary Boolean function. Two learning methods are presented whereby these networks can be trained easily. The broad applicability of these networks is proven by several case studies in different fields of application: image processing, edge detection, image enhancement, super resolution, pattern recognition, face recognition, and prediction. The book is hence partitioned into three almost equally sized parts: a mathematical study of the unique features of these new neurons, learning of networks of such neurons, and application of such neural networks. Most of this work was developed by the first two authors over a period of more than 10 years and was only available in the Russian literature. With this book we present the first comprehensive treatment of this important class of neural networks in the open Western literature. Multi-Valued and Universal Binary Neurons is intended for anyone with a scholarly interest in neural network theory, applications and learning. It will also be of interest to researchers and practitioners in the fields of image processing, pattern recognition, control and robotics Physics Artificial intelligence Computer engineering Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Electrical Engineering Signal, Image and Speech Processing Künstliche Intelligenz Aizenberg, Naum N. Sonstige oth Vandewalle, Joos Sonstige oth https://doi.org/10.1007/978-1-4757-3115-6 Verlag Volltext |
| spellingShingle | Aizenberg, Igor N. Multi-Valued and Universal Binary Neurons Theory, Learning and Applications Physics Artificial intelligence Computer engineering Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Electrical Engineering Signal, Image and Speech Processing Künstliche Intelligenz |
| title | Multi-Valued and Universal Binary Neurons Theory, Learning and Applications |
| title_auth | Multi-Valued and Universal Binary Neurons Theory, Learning and Applications |
| title_exact_search | Multi-Valued and Universal Binary Neurons Theory, Learning and Applications |
| title_full | Multi-Valued and Universal Binary Neurons Theory, Learning and Applications by Igor N. Aizenberg, Naum N. Aizenberg, Joos Vandewalle |
| title_fullStr | Multi-Valued and Universal Binary Neurons Theory, Learning and Applications by Igor N. Aizenberg, Naum N. Aizenberg, Joos Vandewalle |
| title_full_unstemmed | Multi-Valued and Universal Binary Neurons Theory, Learning and Applications by Igor N. Aizenberg, Naum N. Aizenberg, Joos Vandewalle |
| title_short | Multi-Valued and Universal Binary Neurons |
| title_sort | multi valued and universal binary neurons theory learning and applications |
| title_sub | Theory, Learning and Applications |
| topic | Physics Artificial intelligence Computer engineering Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Electrical Engineering Signal, Image and Speech Processing Künstliche Intelligenz |
| topic_facet | Physics Artificial intelligence Computer engineering Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Electrical Engineering Signal, Image and Speech Processing Künstliche Intelligenz |
| url | https://doi.org/10.1007/978-1-4757-3115-6 |
| work_keys_str_mv | AT aizenbergigorn multivaluedanduniversalbinaryneuronstheorylearningandapplications AT aizenbergnaumn multivaluedanduniversalbinaryneuronstheorylearningandapplications AT vandewallejoos multivaluedanduniversalbinaryneuronstheorylearningandapplications |