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Models of neurons and perceptrons = ...
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Bielecki, Andrzej.
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Models of neurons and perceptrons = selected problems and challenges /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Models of neurons and perceptrons/ by Andrzej Bielecki.
Reminder of title:
selected problems and challenges /
Author:
Bielecki, Andrzej.
Published:
Cham :Springer International Publishing : : 2019.,
Description:
vi, 156 p. :ill., digital ;24 cm.
[NT 15003449]:
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.
Contained By:
Springer eBooks
Subject:
Neurons - Simulation methods. -
Online resource:
http://dx.doi.org/10.1007/978-3-319-90140-4
ISBN:
9783319901404
Models of neurons and perceptrons = selected problems and challenges /
Bielecki, Andrzej.
Models of neurons and perceptrons
selected 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.770..
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
Standard No.: 10.1007/978-3-319-90140-4doiSubjects--Topical Terms:
3377916
Neurons
--Simulation methods.
LC Class. No.: QP363 / .B54 2019
Dewey Class. No.: 573.8536
Models of neurons and perceptrons = selected problems and challenges /
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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.
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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.
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W9366002
電子資源
11.線上閱覽_V
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EB QP363 .B54 2019
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