Abstract: Concerning the problem in the conventional neural network expert system that can not provide explaining facility and reasoning process, the hot rolling products quality test system based on Radial Basis Function (RBF) neural network with function of qualitytrack can overcome the shortcoming. In the part of quality test, it provided detailed explanations and tracking process of the output of the expert system. According to the characteristics of steel industry, it used multiRBF neural network in the part of physical properties test. Firstly it used RBF neural network to forecast physical properties, and then dealt with the input parameters with the multiquadratic function. Finally it made longitudinal tracing and horizontal tracing to the result. The practical application result shows this system improves the degree of automation and the efficiency quality test in iron and steel enterprise. Compared with the previous way, it saves 60% of the time.

Key words: expert system, Radial Basis Function (RBF) neural network, multiquadratic function, quality inspection, quality traceability

摘要： 针对以往神经网络专家系统解释机制不健全以及无法提供推理过程的问题，提出了结合质量追溯功能的基于径向基函数(RBF)神经网络的热轧成品质量检测专家系统，在质量追溯部分针对专家系统的输出结果给出详细的解释及追溯过程。根据钢铁行业的特点，对物理性能检测部分应用复合神经网络，首先通过RBF神经网络对物理性能参数进行预测，然后用复二次函数作为核函数处理输入参数，并对输出结果进行纵向追溯和横向追溯。系统实际应用结果表明，该专家系统提高了钢铁企业质检工作的自动化程度和效率，与以往的人工质检方式相比节约了60%的时间。

关键词: 专家系统, 径向基神经网络, 复二次函数, 质量检测, 质量追溯