摘要
针对目前船舶设备故障诊断方法存在适用性不广、准确度不高等问题,引入径向基神经网络船舶设备故障诊断方法。提出一种基于反向学习和自适应搜索策略结合的改进人工蜂群算法,通过反向学习策略进行蜜源初始化,提高初始解的质量,并在迭代过程中自适应调整搜索步长,提升原算法的收敛性能和局部寻优能力。将该算法与径向基神经网络的参数寻优相结合,构造性能良好的故障诊断分类器。实验结果表明,该方法有效提高了故障诊断的准确性和适用性,满足船舶设备故障诊断的实时性能要求。
For the ships equipment fault diagnosis problems with lack of applicability and accuracy during ships sailing, this pa- per designs a radical basis function neural network method for ships equipment fault diagnosis. An improved artificial bee colony （IABC） algorithm combining opposite learning initialization strategy and auto-adapted search strategy is designed, which builds higher quality initial solution space through opposite learning initialization strategy and adapts its local search length automatically to improve the ability of convergence and local optimization searching. IABC algorithm is used in parameter optimization of radical basis function neural network （RBFNN） for constructing a better performed classifier. The results show that the IABC-RBFNN framework can improve the accuracy and usability of ship fault diagnosis, and satisfy the real-time requirement of ships equipment fault diagnosis.
出处
《计算机与现代化》
2017年第6期8-14,19,共8页
Computer and Modernization
基金
江苏省产学研联合创新资金资助项目(SBY201320423)
关键词
船舶设备故障诊断
径向基神经网络
人工蜂群算法
反向学习策略
自适应策略
ship equipment fault diagnosis
radical basis function neural network
artificial bee colony algorithm
opposite learn- ing strategy
auto-adapted strategy
