中文摘要：

针对现有变权缓冲算子简单的结构导致其不能充分利用系统行为信息的问题,基于新信息优先原理,构造一类带有权重调节因子的全信息变权弱化缓冲算子和强化缓冲算子.考虑到权重调节因子与预测误差之间的非线性关系,以平均预测误差最小化为优化目标,应用遗传算法在（0,1）区间内搜索权重调节因子的最优值.以我国工业企业总产值和浙江省外商直接投资预测问题为例,分别验证了全信息变权弱化缓冲算子和强化缓冲算子处理小样本扰动数据建模问题的有效性.结果表明：全信息变权缓冲算子对序列的作用过程体现了新信息优先原理,通过优化权重调节因子能够有效地控制算子作用强度,且对冲击扰动系统的预测能力优于传统的高阶缓冲算子.

英文摘要：

To deal with the problem of the insufficient utilization of the behavior systems＇information due to the simple structures of existing variable-weight buffer operators, a class of variable weights weakening and strengthening buffer operators with weights＇ regulating factors and perfect information were constructed based on the new information priority principle. Considering the nonlinear relationships between the weights＇ regulating factors and prediction errors, the genetic algorithm was employed to search the optimal regulating factor in interval （0,1）, under the optimization objective of minimizing the average prediction error. The predition problems of the gross output of industrial enterprises in China and the foreign direct investment in Zhejiang Province of China were studied to verify the effectiveness of the variable-weight buffer operators with perfect information for dealing with the modeling problem of small sample disturbed data. The results show that the action process of the new buffer operators affecting original data embodies the new information priority principle. The optimization of the weight regulating factors can effectively control the interaction strength. The forecasting ability of the new buffer operators for disturbed systems is superior to the traditional high order buffer operators.