LU Jin-na(卢金娜）, HU Hong-ping(胡红萍), BAI Yan-ping（白艳萍）
(School of Science, North University of China, Taiyuan 030051, China)
Abstract：This paper proposes the generalized regression neural network(GRNN) model and multi-GRNN model with a gating network by selecting the data of Shanghai index, the stocks of Shanghai Pudong Development Bank(SPDB), Dongfeng Automobile and Baotou Steel. We analyze the two models using Matlab software to predict the opening price respectively. Through building a softmax excitation function，the multi-GRNN model with a gating network can obtain the best weights. Using the data of the four groups, the average of forecasting errors of 4 groups by GRNN neural model is 0.012 208, while the average of the multi-GRNN models’s with a gating network is 0.002 659. Compared with the real data, it is found that the both results predicted by the two models have small mean square prediction errors. So the two models are suitable to be adopted to process a large quantity of data, furthermore the multi-GRNN model with a gating network is better than the GRNN model.
Key words：stock forecasting; GRNN model; gating network; softmax incentive
CLD number： TP183 Document code： A
Article ID： 1674-8042(2012)04-0374-05 doi： 10.3969/j.issn.1674-8042.2012.04.015
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