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论文类型：会议论文

发表时间：2007-09-14

收录刊物：EI、CPCI-S、Scopus

页面范围：82-86

摘要：This paper is concerned with the approximation capability of feedforward neural networks to a compact set of functions. We follow a general approach that covers all the existing results and gives some new results in this respect. To elaborate, we have proved the following: If a family of feedforward neural networks is dense in H, a complete linear metric space of functions, then given a compact set V subset of H and an error bound epsilon, one can fix the quantity of the hidden neurons and the weights between the input and hidden layers, such that in order to approximate any function f is an element of V with accuracy epsilon, one only has to further choose suitable weights between the hidden and output layers. We also apply our theorem to the problem of system identification, or approximation to an operator, by neural networks.