Titlebook: Dealing with Complexity; A Neural Networks Ap Mirek Kárny,Kevin Warwick,Vera K?rková Book 1998 Springer-Verlag London Limited 1998 artifici

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Approximation of Smooth Functions by Neural Networks,ies ..,..,... is to consider each .. as an unknown fuction of a certain (fixed) number of previous values. A neural network is then trained to approximate this unknown function. We note that one of the reasons for the popularity of neural networks over their precursors, perceptrons, is their universal approximation property.

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Lecture Notes in Computer Scienceies ..,..,... is to consider each .. as an unknown fuction of a certain (fixed) number of previous values. A neural network is then trained to approximate this unknown function. We note that one of the reasons for the popularity of neural networks over their precursors, perceptrons, is their universal approximation property.

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Numerical Aspects of?Hyperbolic Geometryr, in many cases, the neural network is treated as a black box, since the internal mathematics of a neural network can be hard to analyse. As the size of a neural network increases, its mathematics becomes more complex and hence harder to analyse. This chapter examines the use of concepts from state

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Philipp Andelfinger,Justin N. Kreikemeyercan be viewed as universal approximators of non-linear functions that can learn from examples. This chapter focuses on an iterative algorithm for training neural networks inspired by the strong correspondences existing between NNs and some statistical methods [1][2]. This algorithm is often consider

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https://doi.org/10.1007/978-1-0716-4003-6s probabilistic interpretation depends on the cost function used for training. Consequently, there has been considerable interest in analysing the properties of the mean square error criterion. It has been shown by several authors that, when training a multi-layer neural network by minimizing a mean

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