In this paper, we introduce a factorization for the infinite Hilbert matrix based on Cesàro matrices. Moreover, through the relation between Cesàro and Gamma matrices, we extract our second factorization for the Hilbert matrix based on Gamma matrices. The results of these factorizations are two new inequalities one of which is a generalized version of the well-known Hilbert’s inequality.
The nature of radial basis function (RBF) networks necessitates some types of errors which can never be removed by traditional training algorithms. This paper is an attempt to introduce the natural error sources of neural networks such as bias error, iteration-restricted error, and Gibbs' error. Moreover, a new method is introduced, called post-training, to reduce these errors as far as desired
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