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河北工业大学
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基于属性偏好学习的配电网综合评价方法.

Title: 基于属性偏好学习的配电网综合评价方法.
Alternate Title: Attribute preference learning based comprehensive evaluation method for distribution network.
Language: Chinese
Authors: 谈元鹏1 foxdaemons@163.com
李买林1
许 刚1
Source: Application Research of Computers / Jisuanji Yingyong Yanjiu. Mar2017, Vol. 34 Issue 3, p785-790. 6p.
Document Type: Article
Author-Supplied Keywords: attribute measure ; distribution network evaluation ; evaluation preference ; multiple attribute decision making ; neural networks ; 多属性决策 ; 属性测度 ; 神经网络 ; 评价偏好 ; 配电网评价 ; attribute measure ; distribution network evaluation ; evaluation preference ; multiple attribute decision making ; neural networks ; Language of Keywords: English; Chinese
Abstract (English): To avoid the over reliance of personal preferences on traditional regional distribution network evaluation and achieve reasonable, accurate attribute weights, this paper proposed a multiple index attribute preference learning based intelligent comprehensive evaluating method for distribution network. The proposed method established the distribution network comprehensive evaluation model under confidence criterion and score criterion, according to the attribute measure theory. Then, it pre-processed the intermediate value indexes by introducing numerical absolute deviation rate. Finally, based on the historical training samples of distribution network, the proposed method calculated the preference weights of indexes by employing supervised random weighted neural network learning model. The paper performed intelligent evaluation of test samples by using the distribution network comprehensive evaluation model and well-trained attribute preference weights. Compared with traditional AHP. PSO-SVM and RWN algorithms, the experimental result analysis demonstrates that the proposed method is feasible, effective and robust, which can achieve a reasonable and objective comprehensive evaluation of target distribution network, and has certain application value on regional distribution network evaluation. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 为了摆脱在传统地区配电网评价方法中对参评人员个人评价偏好的过度依赖,实现合理、精准的属性 权重确定,提出了一种基于属性偏好学习的配电网多指标智能综合评价方法。依据属性测度理论,在置信度准 则与评分准则下完成对配电网综合评价模型的构造;进而提出数值绝对偏移率指标以实现对中间值指标的数据 预处理;最后,应用随机权神经学习方法,通过对配电网历史训练样本进行有监督学习,计算得到指标属性偏好 权重,并依据配电网综合评价模型以及计算所得属性偏好权重完成对配电网待测样本的智能综合评价。与传统 的AHP、PSOSVM以及RWN算法的对比仿真实验验证了该方法的精确性与稳定性。该方法实现了合理、客观 的配电网综合评价,对地区配电网评价具有一定的实际应用价值。 [ABSTRACT FROM AUTHOR]
(Copyright applies to all Abstracts.)
Author Affiliations: 1华北电力大学电气与电子工程学院,北京102206
ISSN: 1001-3695 (Sherpa/RoMEO, JCR)
PageCount: 785-791
volume: 34
issue: 3
issn: 10013695
pubdate: 2017
DOI: https://doi.org/10.3969/j.issn.1001-3695.2017.03.034
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