Relaxed conditions for radial-basis function networks to be universal approximators

Neural Networks - Tập 16 - Trang 1019-1028 - 2003
Yi Liao1, Shu-Cherng Fang1, Henry L.W. Nuttle1
1Operations Research and Industrial Engineering, North Carolina State University, Raleigh, NC 27695-7906, USA

Tài liệu tham khảo

Chen, 1995, Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems, IEEE Transactions on Neural Networks, 6, 911, 10.1109/72.392253 Chen, 1991, Orthogonal least squares learning algorithm for radial basis function networks, IEEE Transactions on Neural Networks, 2, 302, 10.1109/72.80341 Cybenko, 1989, Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals, and Systems, 3, 303, 10.1007/BF02551274 Friedman, 1963 Hornik, 1990, Approximation capabilities of multilayer feedforward neural networks, Neural Networks, 4, 251, 10.1016/0893-6080(91)90009-T Hornik, 1993, Some new results on neural network approximation, Neural Networks, 6, 1069, 10.1016/S0893-6080(09)80018-X Leshno, 1993, Multilayer feedforward networks with a polynomial activation function can approximate any function, Neural Networks, 6, 861, 10.1016/S0893-6080(05)80131-5 Mhaskar, 1992, Approximation by superposition of sigmoidal and radial basis functions, Advances in Applied Mathematics, 13, 350, 10.1016/0196-8858(92)90016-P Murphy, P. M., Aha, D. W. (1992). UCI repository of machine learning databases. URL: http://www.ics.uci.edu/~mlearn/MLRepository.html. Park, 1991, Universal approximation using radial-basis-function networks, Neural Computation, 3, 246, 10.1162/neco.1991.3.2.246 Park, 1993, Approximation and radial-basis-function networks, Neural Computation, 5, 305, 10.1162/neco.1993.5.2.305 Rudin, 1987