Case-based adaptation for product formulation
Case-based adaptation for product formulation
复制标题
基于案例的产品配方调整
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
C. Hinde
中科院分区：
文献类型：
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作者：
D. Velandia;A. West;C. Hinde
基于实例推理（CBR）在设计领域的适应是知识密集型的事实是限制CBR系统的工业应用的主要因素之一。然而，归纳技术可以缓解适应知识获取的瓶颈，使有用的知识，从案例库（CB）。本文研究了神经网络的应用，它使用CB中的知识来（i）从查询和检索到的案例之间的差异生成所需的映射，（ii）最小化这些差异，从而（iii）调整检索到的案例，以便找到查询的最佳解决方案。这种适应方法适用于使用数值属性描述案例的CBR系统。
The fact that case-based reasoning (CBR) adaptation in design domains is knowledge-intensive is one of the major factors that has limited the industrial application of CBR systems. Nevertheless, inductive techniques can ease the adaptation knowledge acquisition bottleneck by enabling useful knowledge to be elicited from the case-base (CB). Application of neural networks that use the knowledge available in the CB to (i) generate a desired mapping from differences between a query and retrieved cases, (ii) to minimise those differences and hence (iii) to adapt retrieved cases so that an optimal solution to a query is found is studied in this paper. This adaptation method is suitable for CBR systems that use numerical-valued attributes for describing a case.