针对粒子群优化(PSO)算法在自适应神经模糊推理系统(ANFIS)中的集成应用，提出对学习神经模型参数、隶属度函数参数进行改进优化的算法。该算法可增强模糊系统的近似精度和可解释性，提高系统的性能，进而发现更好的分类优化规则。算法经4个标准数据库的数据测试，结果表现出更好的性能，获得更好的分类效果，同时降低了系统时间复杂度。%For the particle swarm optimization (PSO) algorithm on the adaptive neuro fuzzy inference system (ANFIS) integrated ap-plications, put forward an improved algorithm by optimizing the learning of neural model parameters, the parameters of membership functions. The algorithm can improve the approximation accuracy and interpretability of fuzzy systems and the performance of the sys-tem. The proposed method has been tested on four standard dataset from UCI machine learning, i.e. Iris Flower, Haberman’s Survival Data, Balloon and Thyroid dataset. The results have shown better classification using the proposed PSO-ANFIS and the time complexity has reduced accordingly.
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