This paper proposes a novel fuzzy system modeling algorithm ANFIS-HC-QPSO:a hybrid fuzzy clustering algorithm is used to divide the input space and every cluster generates a membership function by approximation to recog-nize the premise parameters of ANFIS roughly. The Quantum behaved Particle Swarm Optimization algorithm(QPSO)is applied with least square method to optimize the rough premise parameters until obtaining all the parameters of ANFIS. The experiments indicate that, compared with traditional methods, ANFIS-HC-QPSO can build the ANFIS model with less fuzzy rules, but much more precise.%为了进一步提高模糊系统建立模型的精度，提出一种新的模糊系统算法ANFIS-HC-QPSO：采用一种混合型模糊聚类算法来对模糊系统的输入空间进行划分，每一个聚类通过高斯函数的拟合产生一个隶属度函数，即完成ANFIS系统的前件参数--隶属度函数参数的初始识别，通过具有量子行为的粒子群算法QPSO与最小二乘法优化前件参数，直至达到停机条件，最终得到ANFIS的前件及后件参数，从而得到满意的模糊系统模型。实验表明，AN-FIS-HC-QPSO算法与传统算法相比，能在只需较少模糊规则的前提下就使模糊系统达到更高的精度。
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