Function Approximation by Random Neural Networks with a Bounded Number of Layers
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چکیده
This paper discusses the function approximation properties of the Gelenbe random neural network GNN We use two extensions of the basic model the bipolar GNN BGNN and the clamped GNN CGNN We limit the networks to being feedforward and consider the case where the number of hidden layers does not exceed the number of input layers With these constraints we show that the feedforward CGNN and the BGNN with s hidden layers total of s layers can uniformly approximate continuous functions of s variables
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تاریخ انتشار 2001