一种结合云模型和 QPSO 优化的 RBFNN及其应用

摘要

提出一种基于云变异操作的量子行为粒子群优化算法（ QPSO-CM）的径向基函数神经网络（ RBFNN）学习方法。首先QPSO算法利用云模型加入云变异操作，增加算法多样性；然后利用减聚类算法确定RBF神经网络径向基层的单元数；最后用QP-SO-CM算法对RBF神经网络的参数（中心与宽度）和连接权重进行优化。将此算法用于齿轮的故障诊断，仿真诊断结果表明此方法是有效的，具有较好的分类效果，诊断精度高、收敛速度快。%In this paper we introduce a learning method of radial basis function ( RBF) neural network which is based on quantum-behaved particle swarm optimisation with cloud mutation operation (QPSO-CM).First, the QPSO adds the cloud mutation operation by using cloud model to increase its diversity;then the method uses the subtractive clustering method to determine the unit number of radial basis layer in RBF neural network;finally, it optimises the parameters ( central position and directional width ) of RBF neural network and the connection weight by QPSO-CM.Applying the method to gear faults diagnosis , the simulation results show that this method is effective with high diagnosis accuracy and fast convergence .