B# 7"wdqd'qr, On the approximate realization of continuous mappings by neural networks, Neural Networks, 2 (1989), pp. 183--192. Home/Search Document Not in Database Summary Related Articles Check |
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Departement Elektrotechniek ESAT-SISTA/TR 1993-46 - Neural Network Models (Correct)
....be shown how nonlinear system identification results using multilayer neural networks can be used for this purpose. Moreover up till now it is not really clear if neural network models which are capable to approximate any continuous nonlinear function arbitrarily well by one or more hidden layers [5][8] are to be prefered over other parametrizations such as polynomial representations, radial basis functions, wavelets, etc. for doing identification. The results in this paper suggest that one of the advantages of multilayer neural nets lies precisely in control. The neural net models can be ....
Funahashi K-I., On the Approximate Realization of continuous Mappings by Neural Networks, Neural Networks, Vol.2, pp 183-192, 1989.
Artificial Neurons with Arbitrarily - Complex Internal Structures (Correct)
....when the value of all internal variables are equal to their values in some predefined configuration. A GAN of this type represents what is sometimes called a grandmother cell. Leshno et al. s proof is the most general in a series of such proofs. For earlier, more restrictive results see e.g. [3, 10, 9] 4.3 Other Forms of Obviously, there are an infinite number of functions one could use for f , some of which can take us beyond conventional neurons and networks, to a more general view of computation in neural network like settings. Return for a moment to the example discussed in section ....
FUNAHASHI, K. On the approximate realization of continuous mappings by neural networks. Neural Networks 2 (1989), 183--192.
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....area. However, most of these attempts have stayed within the context of propositional logic and they are unlikely to carry over to full rst order logic. One exception to this statement is due to H olldobler, St orr and Kalinke in [3] who employed a general approximation theorem due to Funahashi [4] which states that every continuous function on the real numbers can be uniformly approximated by 3 layer feedforward neural networks. H olldobler et al. investigated a syntactically restricted class of logic programs (acyclic normal with injective level mapping) and showed that for these a ....
....input layer output layer c w Fig. 1. 3 Layer Feedforward Neural Network. It is our aim to obtain results on the approximation of consequence operators by input output functions of 3 layer feedforward networks. Our rst result rests on the following theorem, which is due to Funahashi, see [4]. Theorem 4. Suppose that : R R is non constant, bounded, monotone increasing and continuous. Let K R be compact, let f : K R be a continuous mapping and let 0. Then there exists a 3 layer feedforward network with squashing function whose input output mapping f : K R satis ....
Funahashi, K.I.: On the approximate realization of continuous mappings by neural networks. Neural Networks 2 (1989) 183-192
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B# 7"wdqd'qr, On the approximate realization of continuous mappings by neural networks, Neural Networks, 2 (1989), pp. 183--192.
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K. Funahashi. On the approximate realization of continuous mappings by neural networks. Neural Networks, 2:183-192, 1989.
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K. Funahashi. On the approximate realization of continuous mappings by neural networks. Neural Networks, vol. 2, pp. 183-192, 1989.
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