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130626  eng 
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a 9783642214318

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1 

a Anastassiou, George A.

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0 
0 
a Intelligent Systems: Approximation by Artificial Neural Networks
h Elektronische Ressource
c by George A. Anastassiou

250 


a 1st ed. 2011

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a Berlin, Heidelberg
b Springer Berlin Heidelberg
c 2011, 2011

300 


a VIII, 108 p
b online resource

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0 

a Univariate sigmoidal neural network quantitative approximation  Univariate hyperbolic tangent neural network quantitative approximation  Multivariate sigmoidal neural network quantitative approximation  Multivariate hyperbolic tangent neural network quantitative approximation

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a Computational intelligence

653 


a Artificial Intelligence

653 


a Computational Intelligence

653 


a Artificial intelligence

653 


a Applications of Mathematics

653 


a Mathematics

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0 
7 
a eng
2 ISO 6392

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b Springer
a Springer eBooks 2005

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a Intelligent Systems Reference Library

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0 
a 10.1007/9783642214318

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u https://doi.org/10.1007/9783642214318?nosfx=y
x Verlag
3 Volltext

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0 

a 006.3

520 


a This brief monograph is the first one to deal exclusively with the quantitative approximation by artificial neural networks to the identityunit operator. Here we study with rates the approximation properties of the "right" sigmoidal and hyperbolic tangent artificial neural network positive linear operators. In particular we study the degree of approximation of these operators to the unit operator in the univariate and multivariate cases over bounded or unbounded domains. This is given via inequalities and with the use of modulus of continuity of the involved function or its higher order derivative. We examine the real and complex cases. For the convenience of the reader, the chapters of this book are written in a selfcontained style. This treatise relies on author's last two years of related research work. Advanced courses and seminars can be taught out of this brief book. All necessary background and motivations are given per chapter. A related list of references is given also per chapter. The exposed results are expected to find applications in many areas of computer science and applied mathematics, such as neural networks, intelligent systems, complexity theory, learning theory, vision and approximation theory, etc. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, also for all science libraries
