%0 Journal Article
%T 基于L1/2范数正则化的塑性回声状态网络故障诊断模型<br>A fault diagnosis model of plasticity echo state network based on L1/2-norm regularization
%A 逯程
%A 徐廷学
%A 王虹
%J 北京航空航天大学学报
%D 2018
%R 10.13700/j.bh.1001-5965.2017.0214
%X 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L1/2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L1/2范数惩罚项以提高稀疏化效率，利用一个光滑化的L1/2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。<br>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 L1/2-norm regularization is presented. BCM rule was introduced into the reservoir construction to train the connection weight matrix. Meanwhile, the L1/2-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 smoothing L1/2 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.
%K 储备池
%K 回声状态网络(ESN)
%K BCM规则
%K L1/2范数正则化
%K 半阈值迭代法
%K 故障诊断&lt
%K br&gt
%K reservoir
%K echo state network (ESN)
%K BCM rule
%K L1/2-norm regularization
%K half threshold iteration method
%K fault diagnosis
%U http://bhxb.buaa.edu.cn/CN/abstract/abstract14436.shtml