首页> 外文会议>Advances in Neural Networks - ISNN 2007 pt.2; Lecture Notes in Computer Science; 4492 >A Novel Approach for Extraction of Fuzzy Rules Using the Neuro-fuzzy Network and Its Application in the Blending Process of Raw Slurry
【24h】

A Novel Approach for Extraction of Fuzzy Rules Using the Neuro-fuzzy Network and Its Application in the Blending Process of Raw Slurry

机译:神经模糊网络提取模糊规则的新方法及其在生料混合中的应用

获取原文
获取原文并翻译 | 示例

摘要

A novel approach is proposed to extract fuzzy rules from the input-output data using the neuro-fuzzy network combined the improved c-means clustering algorithm. Interpretability, which is one of the most important features of fuzzy system, is obtained using this approach. The fuzzy sets number of variables can also be determined appropriately using this approach. Finally, the proposed approach is applied to the blending process of raw slurry in the alumina sintering production process. The fuzzy system, which is used to determine the set values of the flow rate of materials, is extracted from the error of production index -adjustment of the flow rate. Application results show that the fuzzy system not only improved the quality of raw slurry but also have good interpretability.
机译:提出了一种结合改进的c-means聚类算法的神经模糊网络从输入输出数据中提取模糊规则的新方法。使用这种方法可获得可解释性,这是模糊系统的最重要特征之一。变量的模糊集数量也可以使用此方法适当确定。最后,将所提出的方法应用于氧化铝烧结生产过程中原料浆的共混过程。从生产指标误差-流量调整中提取了用于确定物料流量设定值的模糊系统。应用结果表明,模糊系统不仅提高了原浆的质量,而且具有良好的解释性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号