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For the nearly exponential type of feedforward neural networks (neFNNs), it is revealed the essential order of their approximation. It is proven that for any continuous function defined on a compact set of Rd, there exists a three-layer neFNNs with fixed number of hidden neurons that attain the essential order. When the function to be ap- proximated belongs to the α-Lipschitz family (0 < α ≤ 2), the essential order of approxi- mation is shown to be O(n?α) where n is any integer not less than the reciprocal of the predetermined approximation error. The upper bound and lower bound estimations on approximation precision of the neFNNs are provided. The obtained results not only char- acterize the intrinsic property of approximation of the neFNNs, but also uncover the im- plicit relationship between the precision (speed) and the number of hidden neurons of the neFNNs.
For the nearly exponential type of feedforward neural networks (neFNNs), it is revealed that the essential order of their approximation. It is proven that for any continuous function defined on a compact set of Rd, there exists a three-layer neFNNs with fixed number of hidden neurons that attain the essential order. When the function to be ap-proximated belongs to the α-Lipschitz family (0 <α ≤ 2), the essential order of approxi- mation is shown to be O (n? α) where n is any integer not less than the reciprocal of the predetermined approximation error. The upper bound and lower bound estimations on approximation precision of the neFNNs are provided. The obtained results not only char- acterize the intrinsic property of approximation of the neFNNs, but also uncover the im- plicit relationship between the precision (speed) and the number of hidden neurons of the neFNNs.