基于神经模糊逻辑的显著误差检测方法

Neural Fuzzy Logic Based Gross Error Detection Method

将神经网络和模糊逻辑有机结合起来的神经网络模糊逻辑系统，具有自组织和监督学习样本、自动构造和发展模糊规则、发现和优化输入输出隶属函数等优点，从而使系统具有较快的收敛速度。将它应用于化工稳态过程中测量数据的显著误差检测，并与传统的显著误差检测方法相比，结果表明了此方法的有效性。

The neural network-based fuzzy logic system,combined with neural network and fuzzy logic, has the advantages Of self-organizing, sample-learning, auto-structuring and fuzzy rule developing and member function optimizing. This method has been used in a steady-state chemical process and proved to be effective.