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APPROXIMATION CAPABILITIES OF MULTILAYER FEEDFORWARD REGULAR FUZZY NEURAL NETWORKS 被引量:2
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作者 Liu PuyinDept. of Math., National Univ. of Defence Technology,Changsha 410073 Dept. of Math., Beijing Normal Univ.,Beijing 100875. 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2001年第1期45-57,共13页
Four layer feedforward regular fuzzy neural networks are constructed. Universal approximations to some continuous fuzzy functions defined on F 0 (R) n by the four layer fuzzy neural networks are shown. At f... Four layer feedforward regular fuzzy neural networks are constructed. Universal approximations to some continuous fuzzy functions defined on F 0 (R) n by the four layer fuzzy neural networks are shown. At first,multivariate Bernstein polynomials associated with fuzzy valued functions are empolyed to approximate continuous fuzzy valued functions defined on each compact set of R n . Secondly,by introducing cut preserving fuzzy mapping,the equivalent conditions for continuous fuzzy functions that can be arbitrarily closely approximated by regular fuzzy neural networks are shown. Finally a few of sufficient and necessary conditions for characterizing approximation capabilities of regular fuzzy neural networks are obtained. And some concrete fuzzy functions demonstrate our conclusions. 展开更多
关键词 Regular fuzzy neural networks cut preserving fuzzy mappings universal approximators fuzzy valued Bernstein polynomials.
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