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초록· 키워드
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In this paper, we study the simultaneous approximation to functions
in C^m [0,1] by neural networks with a squashing function and the complexity related to
the simultaneous approximation using a Bernstein polynomial
and the modulus of continuity.
Our proofs are constructive.
목차
등록된 정보가 없습니다.
참고문헌 (14)
참고문헌 신청
A. R. Gallant / 1992 / On learning the derivatives of an unknown mapping with multilayer feedforward networks / Lett. Math. Phys 5 : 129 ~ 138
B. Gao / 1993 / Univariant approximation by superpositions of a sigmoidal function / J. Math. Anal. Appl 178 (1) : 221 ~ 226
B. Malakooti / 1998 / Approximation polynomial functions by feedforward artificial neural networks : capacity analysis and design / Appl. Math. and Comp 90 : 27 ~ 51
D. E. Rumelhart / 1986 / Parallel Distributed Processing : explorations in the microstructure of cognition / MIT Press
F. Li / 2007 / The essential order of simultaneous approximation for neural networks / Appl. Math. Comput 194 (1) : 120 ~ 127
B. Gao / 1993 / Univariant approximation by superpositions of a sigmoidal function / J. Math. Anal. Appl 178 (1) : 221 ~ 226
B. Malakooti / 1998 / Approximation polynomial functions by feedforward artificial neural networks : capacity analysis and design / Appl. Math. and Comp 90 : 27 ~ 51
D. E. Rumelhart / 1986 / Parallel Distributed Processing : explorations in the microstructure of cognition / MIT Press
F. Li / 2007 / The essential order of simultaneous approximation for neural networks / Appl. Math. Comput 194 (1) : 120 ~ 127
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