A fault diagnosis model of plasticity echo state network based on L1/2-norm regularization
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摘要:
为了提升储备池的动态适应性能，克服回声状态网络（ESN）输出权值求解的病态不适定问题，平衡其拟合与泛化能力，提出了一种基于
L 1/2范数正则化的塑性回声状态网络故障诊断模型。在储备池构建中引入BCM规则对连接权矩阵进行预训练，并在目标函数中添加L 1/2范数惩罚项以提高稀疏化效率，利用一个光滑化的L 1/2正则子克服迭代数值振荡问题，并采用半阈值迭代法对模型进行求解。将模型应用于机载电台的故障诊断问题中，仿真结果证明了模型的有效性和优越性。Abstract:In order to improve the dynamic adaptability of reservoir, overcome the ill-posed problems of output weights in echo state network (ESN), and balance the fitting and generalization ability, a fault diagnosis model of plasticity echo state network based on
L 1/2-norm regularization is presented. BCM rule was introduced into the reservoir construction to train the connection weight matrix. Meanwhile, theL 1/2-norm penalty term was added to the objective function in order to improve the sparsification efficiency. An iterative numerical oscillation problem was solved by using a smoothingL 1/2 regularizer, and finally the model was solved by using the half threshold iteration method. The model is applied to the fault diagnosis of airborne radio station, and the simulation results prove the validity and superiority of the model. -
表 1 离散化处理的故障数据
Table 1. Fault data after discrete processing
序号 测试参数 故障模块 c1 c2 c3 c4 c5 c6 1 2 0 1 1 1 1 d1 2 1 2 1 2 1 2 d2 174 1 1 1 1 1 2 d2 175 1 2 1 0 1 1 d1 
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表 2 诊断方法性能对比
Table 2. Performance comparison of diagnostic methods
方法 储备池生成时间/s 训练时间/s 诊断正确率/% BPNN 43.76 79.6 传统ESN 0.49 14.13 88.4 BCM-ESN 6.04 14.37 90.5 L1/2-ESN 0.36 16.78 91.2 L1/2-PESN 6.58 16.64 93.1
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