國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Models of neurons and perceptronssel...

Bielecki, Andrzej.

Models of neurons and perceptronsselected problems and challenges /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Models of neurons and perceptronsby Andrzej Bielecki.
其他題名:	selected problems and challenges /
作者:	Bielecki, Andrzej.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	vi, 156 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	NeuronsSimulation methods.
電子資源:	http://dx.doi.org/10.1007/978-3-319-90140-4
ISBN:	9783319901404$q(electronic bk.)

Models of neurons and perceptronsselected problems and challenges /
Bielecki, Andrzej.

Models of neurons and perceptronsselected problems and challenges /[electronic resource] :by Andrzej Bielecki. - Cham :Springer International Publishing :2019. - vi, 156 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.7701860-949X ;. - Studies in computational intelligence ;v. 216..

Introduction -- Part I: Preliminaries -- Foundations of artiﬁcial neural networks -- Part II: Mathematical foundations -- General foundations -- Foundations of dynamical systems theory -- Part III: Mathematical models of the neuron -- Models of the whole neuron -- Models of parts of the neuron -- Part IV: Mathematical models of the perceptron -- General model of the perceptron -- Linear perceptrons -- Weakly nonlinear perceptrons -- Nonlinear perceptrons -- Concluding remarks and comments.

This book describes models of the neuron and multilayer neural structures, with a particular focus on mathematical models. It also discusses electronic circuits used as models of the neuron and the synapse, and analyses the relations between the circuits and mathematical models in detail. The first part describes the biological foundations and provides a comprehensive overview of the artificial neural networks. The second part then presents mathematical foundations, reviewing elementary topics, as well as lesser-known problems such as topological conjugacy of dynamical systems and the shadowing property. The final two parts describe the models of the neuron, and the mathematical analysis of the properties of artificial multilayer neural networks. Combining biological, mathematical and electronic approaches, this multidisciplinary book it useful for the mathematicians interested in artificial neural networks and models of the neuron, for computer scientists interested in formal foundations of artificial neural networks, and for the biologists interested in mathematical and electronic models of neural structures and processes.

ISBN: 9783319901404$q(electronic bk.)

Standard No.: 10.1007/978-3-319-90140-4doiSubjects--Topical Terms:

829732
Neurons
--Simulation methods.

LC Class. No.: QP363 / .B54 2019

Dewey Class. No.: 573.8536

Models of neurons and perceptronsselected problems and challenges /
LDR:02658nmm a2200325 a 4500 001 550053
003 DE-He213
005 20180517093801.0
006 m d
007 cr nn 008maaau
008 191001s2019 gw s 0 eng d
020 $a 9783319901404$q(electronic bk.)
020 $a 9783319901398$q(paper)
024 7 $a 10.1007/978-3-319-90140-4 $2 doi
035 $a 978-3-319-90140-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QP363 $b .B54 2019
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 573.8536 $2 23
090 $a QP363 $b .B587 2019
100 1 $a Bielecki, Andrzej. $3 829731
245 1 0 $a Models of neurons and perceptrons $h [electronic resource] : $b selected problems and challenges / $c by Andrzej Bielecki.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a vi, 156 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.770
505 0 $a Introduction -- Part I: Preliminaries -- Foundations of artiﬁcial neural networks -- Part II: Mathematical foundations -- General foundations -- Foundations of dynamical systems theory -- Part III: Mathematical models of the neuron -- Models of the whole neuron -- Models of parts of the neuron -- Part IV: Mathematical models of the perceptron -- General model of the perceptron -- Linear perceptrons -- Weakly nonlinear perceptrons -- Nonlinear perceptrons -- Concluding remarks and comments.
520 $a This book describes models of the neuron and multilayer neural structures, with a particular focus on mathematical models. It also discusses electronic circuits used as models of the neuron and the synapse, and analyses the relations between the circuits and mathematical models in detail. The first part describes the biological foundations and provides a comprehensive overview of the artificial neural networks. The second part then presents mathematical foundations, reviewing elementary topics, as well as lesser-known problems such as topological conjugacy of dynamical systems and the shadowing property. The final two parts describe the models of the neuron, and the mathematical analysis of the properties of artificial multilayer neural networks. Combining biological, mathematical and electronic approaches, this multidisciplinary book it useful for the mathematicians interested in artificial neural networks and models of the neuron, for computer scientists interested in formal foundations of artificial neural networks, and for the biologists interested in mathematical and electronic models of neural structures and processes.
650 0 $a Neurons $x Simulation methods. $3 829732
650 1 4 $a Engineering. $3 210888
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 567118
650 2 4 $a Complexity. $3 274400
650 2 4 $a Neurobiology. $3 195022
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Studies in computational intelligence ; $v v. 216. $3 380871
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-90140-4
950 $a Engineering (Springer-11647)