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Research on fault diagnosis based on RBF NN optimized by an improved QPSO algorithm

机译：改进QPSO算法优化的基于RBF神经网络的故障诊断研究

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For QPSO (Quantum-behaved Particle Swarm Optimization) algorithm’s disadvantages of premature convergence and easily getting into local extremum,an improved QPSO algorithm called co-evolutionary QPSO algorithm with two populations is presented in this paper.Particles are updated by adopting QPSO algorithm inside populations and by using annexing or cooperation operator between populations.The annexing strategy makes the population with worse performance accept the other population’s optimal information with certain probability; And the cooperation strategy makes the two populations exchange optimal information with each other.Moreover,one population introduces Cauchy mutation when the two populations trap into the same optimal value.Then RBF NN (Radial Basis Function Neural Network) is trained by the improved QPSO algorithm and it is applied to fault diagnose of diesel engine valve.The simulation results showed that the improved QPSO-RBF algorithm enhanced accuracy and speeded up convergence rate of fault diagnosis.
机译：针对量子行为粒子群算法（QPSO）过早收敛，容易陷入局部极值的缺点，提出了一种改进的QPSO算法，称为两个种群的协同进化QPSO算法。通过在种群内部采用QPSO算法对粒子进行了更新。吞并策略使性能较差的人口有一定的概率接受另一人口的最优信息。并且，这种合作策略使两个种群能够相互交换最佳信息。此外，当两个种群陷入相同的最优值时，一个种群会引入柯西突变。然后，使用改进的QPSO算法训练RBF NN（径向基函数神经网络）。仿真结果表明，改进的QPSO-RBF算法提高了故障诊断的准确性，加快了故障诊断的收敛速度。

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