This paper presents the Cp, criterion and a parallel algorithm with regard to the large-scale linear regression model. The scalability for the algorithm is analyzed. And the results for the algorithm on a group of computers are given. The quite good speed-up ratio is obtained.
By defining fuzzy valued simple functions and giving L1(μ) approximations of fuzzy valued integrably bounded functions by such simple functions, the paper analyses by L1(μ)-norm the approximation capability of four-layer feedforward regular fuzzy neural networks to the fuzzy valued integrably bounded function F : Rn → FcO(R). That is, if the transfer functionσ: R→R is non-polynomial and integrable function on each finite interval, F may be innorm approximated by fuzzy valued functions defined as to anydegree of accuracy. Finally some real examples demonstrate the conclusions.
Liu Puyin (Dept. of System Eng. and Math., National Univ. of Defence Tech., Changsha 410073)
