为了提高质量功能配置过程的动态决策水平,实现顾客需求重要度的有效预测,采用最小二乘支持向量机(LS-SVM) 建立顾客需求重要度预测模型,并结合人工免疫机理,以LS-SVM 模型参数为抗体、顾客需求重要度预测误差为抗原,采用克隆算法完成顾客需求重要度LS-SVM 预测模型参数的免疫优化选择. 文中还以数控瓦楞纸板印刷模切机顾客需求重要度预测为例验证了所提方法的可行性,并与基于灰色模型和神经网络模型的顾客需求重要度预测方法进行了比较,结果表明,所提出的基于人工免疫机理与LS-SVM 的顾客需求重要度预测方法可行,且预测效果较好.

In order to improve the dynamic decision level during the quality function deployment and effectivelypredict the importance of customer requirements, a forecast model based on the least square support vector machine(LS-SVM) is constructed. Then, by using the model parameters as the antibody and the importance forecast devia-tion as the antigen, the parameters of LS-SVM model are optimized by means of the clonal selection algorithm. Fi-nally, a numerical printing and die-cutting machinery of corrugated board is used as an example to verify the feasi-bility of the proposed method, and the results are compared with those obtained by the methods based on grey modeland neural network. It is found that the proposed method based on artificial immunity and LS-SVM is feasible andeffective in forecasting the importance of customer requirements.