Experts typically have unequal authority levels in collaborative forecasting tasks. Most current fuzzy collaborative forecasting methods address this problem by applying a (fuzzy) weighted average to aggregate experts’ fuzzy forecasts. However, the aggregation result may be unreasonable, hence fuzzy weighted intersection operators have been proposed for fuzzy collaborative forecasting. This paper proposes that unequal expert authority levels are considered when deriving the membership function rather than the aggregation value. Therefore, the membership of a value in the aggregation result cannot exceed those in experts’ fuzzy forecasts. The proposed approach was applied to forecast the yield of a dynamic random access memory product to validate its effectiveness. Experimental results showed that the proposed methodology outperformed all current best-practice methods considered in every aspect, and in particular achieving 65% mean root mean square error reduction. Thus, a high expert authority level increased the likelihood for the forecast, which could not be satisfactorily addressed by simply applying a higher weight to the forecast.

专家在协作预测任务中通常具有不平等的权限级别。当前大多数模糊协作预测方法都是通过应用（模糊）加权平均值来汇总专家的模糊预测来解决此问题的。但是，聚合结果可能不合理，因此提出了模糊加权交叉算子用于模糊协作预测。本文提出，在推导隶属函数而不是聚合值时，应考虑不平等的专家权限级别。因此，聚合结果中值的成员资格不能超过专家的模糊预测中的值。所提出的方法被应用于预测动态随机存取存储器产品的产量以验证其有效性。实验结果表明，所提出的方法优于在各个方面考虑的所有当前最佳实践方法，尤其是降低了65％的均方根误差。因此，较高的专家权限级别增加了预测的可能性，而仅通过将较高的权重应用于预测就无法令人满意地解决该可能性。