基于模糊神经网络的数字资源服务绩效评价

Shiwei Zhu, Yanqing Zhao, Junfeng Yu, Lei Wang, Moji Wei, Aiping Wang
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引用次数: 0

摘要

本文在数字资源服务绩效评估文献中创新性地提出了一种新的混合绩效评估方法。该方法采用基于模糊规则和人工神经网络的层次评价方法。该方法将模糊逻辑和人工神经网络相结合,克服了模糊规则冗余的缺点。评价指标体系是在借鉴前人研究成果和普遍原则的基础上确定的。建立了模糊神经网络评价模型,实现了数字资源的最终评价目标。此外,为了评估该方法的性能,我们将其结果与GRA-BPN模型进行了比较。实验结果表明,该方法具有较高的精度和执行效率。
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Digital resources serving performance assessing based on fuzzy neural networks
This paper is innovatively to develop a new hybrid performance evaluation method in the literature of assessing the digital resources serving performances. The proposed method employs the hierarchical evaluation method based on fuzzy rules and artificial neural networks. The proposed method integrates the fuzzy logic and the artificial neural networks, which overcomes the shortcomings of redundant fuzzy rules. The evaluation index system is determined based on the universal principle and the research fruits of the former scholars home and abroad. We build a fuzzy neural network evaluation model to achieve the final evaluation goal of the digital resources. In addition, to evaluate the performance of the proposed approach, we compare its results with GRA-BPN model. The experimental results demonstrated that the proposed approach has higher accuracy and execution efficiency.
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