Abstract: The paper introduces a kind of adaptive neural-fuzzy inference systems（ANFIS） based on T-S model to deal with the modeling problem of the complex soda carbonization process.With the measured temperature and soda-output flux data，the system self-initializes the fuzzy rules，and adjusts the membership parameters independently until to find the nonlinear flux-temperature model of the process.The paper discusses the system’s structure and its learning algorithm.Simulation results demonstrate the good practical application value of this approach.

摘要： 针对纯碱碳化过程复杂、建模难的特点，提出一种基于T-S模型的自适应神经模糊推理系统（ANFIS）建模方法。该方法提取碳化过程塔内温度分布及出碱流量的实测数据，通过ANFIS网络自组初始化模糊规则，自适应调整前提隶属度参数和结论参数，最终建立出碱流量随塔内温度变化的非线性模型。文章讨论了该网络的结构和学习算法，通过仿真研究得出其良好的实用价值。