Abstract: A technique for fuzzy cognitive maps learning which is based on the quantum-behaved particle swarm optimization algorithm，has been introduced.The proposed approach is used for updating the nonzero weight values that workings of the approach are applied to an industry control problem，which has much realism meaning.The results support the claim that the proposed technique is a promising methodology for fuzzy cognitive maps learning，and the methodology is effective and efficient，which is better than traditional particle swarm optimization algorithm.

Key words: fuzzy cognitive maps, weight matrices, PSO algorithm, QPSO algorithm

摘要： 介绍的是基于量子粒子群算法模糊认知图的学习方法。其主要的思路是更新模糊认知图中能够使之趋向所要求的稳定状态的非零权值。将所研究的方法运用到工业控制问题，具有很大的现实意义。实验的结果表明，该方法是有效的，并优于传统的粒子群算法。

关键词: 模糊认知图, 权重矩阵, 粒子群优化算法, 量子粒子群优化算法