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Titlebook: Intelligent Systems: Approximation by Artificial Neural Networks; George A. Anastassiou Book 2011 Springer Berlin Heidelberg 2011 Computat

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Multivariate Hyperbolic Tangent Neural Network Quantitative Approximation,ors are defined by using a multidimensional density function induced by the hyperbolic tangent function. Our approximations are pointwise and uniform. The related feed-forward neural network is with one hidden layer. This chapter is based on [6].
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Univariate Sigmoidal Neural Network Quantitative Approximation,of continuity of the engaged function or its high order derivative. The operators are defined by using a density function induced by the logarithmic sigmoidal function. Our approximations are pointwise and with respect to the uniform norm. The related feed-forward neural network is with one hidden layer. This chapter relies on [4].
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Univariate Hyperbolic Tangent Neural Network Quantitative Approximation, modulus of continuity of the engaged function or its high order derivative. The operators are defined by using a density function induced by the hyperbolic tangent function. Our approximations are pointwise and with respect to the uniform norm. The related feed-forward neural network is with one hidden layer. This chapter relies on [4].
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978-3-642-26855-7Springer Berlin Heidelberg 2011
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George A. AnastassiouFirst book dealing exclusively with the quantitative approximation by artificial neural networks to the identity-unit operator.Each chapter is written in a self-contained style, all necessary backgrou
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urban renewal research. The deep learning-based image semantic segmentation method is an important part of computer vision technology, which provides a new method for visual quality evaluation of urban landscape. In this chapter, the core area of Shenyang (within the first ring road) was chosen as t
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