The approximation capability of RBF networks is investigated using a test function and a fixed finite number of training data. The test function used allows to confirm the recently introducedconcept of second derivati...The approximation capability of RBF networks is investigated using a test function and a fixed finite number of training data. The test function used allows to confirm the recently introducedconcept of second derivative dependent placement of RBF centers. Different Gaussian RBF networksare trained varying the width and the number of centers (number of hidden units). The dependenceof the approximation error on these network parameters is studied experimentally.展开更多
文摘The approximation capability of RBF networks is investigated using a test function and a fixed finite number of training data. The test function used allows to confirm the recently introducedconcept of second derivative dependent placement of RBF centers. Different Gaussian RBF networksare trained varying the width and the number of centers (number of hidden units). The dependenceof the approximation error on these network parameters is studied experimentally.
