改进的灵敏度剪枝极限学习机

针对极限学习机(ELM) 网络结构优化问题, 提出一种改进的灵敏度剪枝ELM(ImSAP-ELM). ImSAP-ELM 将??₂ 正则化因子引入SAP-ELM 中, 采用留一准则确定最优隐节点数. 推导基于奇异值分解的输出权重计算公式, 避免矩阵奇异导致求解无效的问题. 将ImSAP-ELM 用于故障预测, 利用多组同类型故障数据建立多个ImSAP-ELM 模型, 基于加权思想融合不同ImSAP-ELM 的预测值. 某型无人机发射机实例表明, 相比于ELM、OP-ELM (最优剪枝ELM) 和SAP-ELM, ImSAP-ELM 耗时最高, 但是ImSAP-ELM 的预测误差小于其他3 种方法.

Improved sensitivity-analysis based pruning extreme learning machine

Aiming at the structure optimization of the extreme learning machine(ELM), an improved sensitivity-analysis based pruning ELM(ImSAP-ELM) algorithm is proposed. The ??₂-regularization factor is introduced into the SAP-ELM by using the ImSAP-ELM. The leave-one-out(LOO) criterion is utilized for selecting an appropriate number of hidden neurons. In addition, the computing expression of output weights based on singular value decomposition(SVD) is deduced, which overcomes the problem that computing result is invalid when the matrix is singular. The proposed ImSAP-ELM is applied to fault prediction. Associated with some groups of known fault data under the same fault type, a number of ImSAP-ELM based models are built. All the prediction values from different ImSAP-ELMs are fused with weighted sum. The case study on a certain unmanned aerial vehicle transmitter shows that, comparing with the ELM, the optimally pruned ELM(OP-ELM) and the SAP-ELM, though ImSAP-ELM time consuming is the highest, the prediction error of the ImSAP-ELM is lower than other 3 algorithms.