基于RBF神经网络的干燥机HOQ模板自动生成模型

为了解决传统干燥机质量功能展开存在的问题,将人工智能理论引入质量功能展开配置过程,提出了智能干燥机质量功能展开的概念,并对其关键技术质量屋模板自动生成模型进行了深入研究,建立了基于径向基网络的干燥机质量屋模板自动生成模型·仿真研究表明,在知识库和数据库的支持下,该模型能够自动将顾客需求转化为相应的工程特性,降低了干燥机质量功能展开配置的复杂程度,减少了对开发人员经验和知识依赖,提高了配置效率,进而能够最大限度地发挥质量功能展开的优势·

Model of Dryer HOQ Templet Automatic Generation Based on RBF-ANN

Quality function deployment (QFD) is a well-known customer-driven methodology for new dryer product development, using house of quality (HOQ) to translate customer requirements into all developing stages of dryer products. To solve the QFD problem of conventional dryers, the artificial intelligence theory is introduced in QFD to form new concept, namely intelligent QFD (IQFD). As a key to IQFD, the technology of dryers' HOQ temple automatic generation is studied in depth so as to set up a relevant model based on the radius basis function and artificial neural network (RBF-ANN). An illustrative example shows that the proposed model can convert customer requirements into corresponding engineering characteristics automatically as supported by the knowledge and date bases. As a result, the complication in application of dryers' QFD lowers and the dependence on experience and knowledge of design team is reduced, thus improving deployment efficiency and taking fully the advantage of QFD.