【摘要】 本文研究基于投影寻踪学习网络的多维非线性自回归模型逼近的收敛性,证明了在 Lk（k为正整数）空间上,投影寻踪学习网络可以以任意精度逼近多维非线性自回归模型,并给出应用实例.更多还原

【Abstract】 In this paper, we study the convergence property of the projection pursuit learning network (PPLN), which is used to approximate to multivariate nonlinear autoregression. The authors first prove that PPLN can approximate to multivariate nonlinear autoregreesion at any given precision in Lk space that k is integer, k≥ 1. The learning strategy and calculation procedures of PPLN, which are used to establish the model and forecast the subsequent behavior of multivariate nonlinear time series {Xt}, are also presented. We derive the consistency results and conclude from simulation and real data analysis that the new method has better convergence property and predictive potential than backpropagation learning network (BPLN).更多还原