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中文摘要：
由于系统内部和外部各种累积性和突发性因素的作用,社会经济系统的突变现象时常发生。针对系统发生突变或变革的当期或短期内,由于预测所需要的基本样本数据量无法满足要求,许多经典预测模型失灵问题,将泛函理论与灰色系统理论相结合,并运用贝叶斯网络推理技术,建立了灰色泛函预测GFAM（1,1）（Grey Function Analysis Model（1,1））模型,充分挖掘和利用系统突变当前时段或突变后较短时间内的信息,以克服传统模型必须在获得足够统计数据后才能进行预测的滞后性缺陷,在系统突发环境下实现科学的推理与预测;给出了利用GFAM（1,1）模型预测的步骤,最后结合案例验证了该模型的适用性。
英文摘要：
Because of the influence of internal and external factors,catastrophe occurs frequently in the economic system.Most of prediction models which use historical data will fail because the sample data needed in prediction cannot meet the requirement.This paper establishes Grey Function Analysis Model（1,1）based on functional theory,Bayesian Network theory and Grey theory.More precise prediction can be made with this model which can fully mine the existing data.The steps of GFAM（1,1）is given in this paper.Finally the model is verified through a case study.
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