随机可变模糊识别模型在清洁生产评价中的应用

针对清洁生产评价中模糊性与随机性并存的问题,建立随机可变模糊识别模型。向可变模糊识别模型中引入一个服从正态分布的随机项,同步研究模糊性和随机性。通过控制模型中距离参数p和优化准则参数α,得到4个随机模糊识别子模型,随后根据正态分布3σ原则,选择合理的置信度,得到级别特征值的置信区间,以此判断隶属级别。该模型与传统百分制评分模型相比削减了主观偏好对评价结果的影响,提高了评价的可操作性和准确性。实例结果证明随机可变模糊识别模型能准确评价清洁生产水平,为清洁生产评价工作提供参考。

Application of random variable fuzzy recognition model in cleaner production assessment

The random variable fuzzy recognition model was established for the coexistence of randomness and fuzziness in cleaner production assessment. A random term obeying normal distribution was introduced to the variable fuzzy recognition model, so the randomness and fuzziness could be studied simultaneously. Four random fuzzy recognition sub-models were generated by controlling the distance parameter p and the optimization parameter α, and then according to the 3σ principle of normal distribution, after choosing a reasonable confidence, the grade was identified by the confidence interval of grade feature value. Compared with the scoring model of centesimal system, this model could cut down the effect of subjective preference on assessment results, and could improve the maneuverability and accuracy in evaluation process. The experimental results showed that the random variable fuzzy recognition model could evaluate the cleaner production level accurately, which could also provide a reference for cleaner production assessment.