基于稀疏核增量超限学习机的机载设备在线状态预测
发布时间：2019-07-04 07:28
【摘要】：为实现对机载设备工作状态的在线状态预测,提出了一种稀疏核增量超限学习机(ELM)算法。针对核在线学习中核矩阵膨胀问题,基于瞬时信息测量提出了一个融合构造与修剪策略的两步稀疏化方法。通过在构造阶段最小化字典冗余,在修剪阶段最大化字典元素的瞬时条件自信息量,选择一个具有固定记忆规模的稀疏字典。针对基于核的增量超限学习机核权重更新问题,提出改进的减样学习算法,其可以实现字典中任一个核函数删除后剩余核函数Gram矩阵的逆矩阵的前向递推更新。通过对某型飞机发动机的状态预测,在预测数据长度等于20的条件下,本文提出的算法将预测的整体平均误差率下降到2.18%,相比于3种流形的核超限学习机在线算法,预测精度分别提升了0.72%、0.14%和0.13%。
[Abstract]:In order to predict the working state of airborne equipment, a sparse kernel incremental learning machine (ELM) algorithm is proposed. In order to solve the problem of kernel matrix expansion in nuclear online learning, a two-step thinning method based on instantaneous information measurement is proposed, which combines construction and pruning strategy. By minimizing dictionary redundancy in the construction stage and maximizing the instantaneous conditional self-information of dictionary elements in the pruning stage, a sparse dictionary with fixed memory scale is selected. In order to solve the problem of kernel weight updating of incremental overlimited learning machines based on kernel, an improved sample reduction learning algorithm is proposed, which can update the inverse matrix of the residual kernel function Gram matrix after the deletion of any kernel function in the dictionary. Through the state prediction of a certain aircraft engine, under the condition that the predicted data length is equal to 20, the overall average error rate of the prediction is reduced to 2.18%. Compared with the on-line kernel overlimited learning machine algorithm of three manifolds, the prediction accuracy is improved by 0.72%, 0.14% and 0.13%, respectively.
【作者单位】： 海军航空工程学院科研部;
【基金】：国家自然科学基金(61571454)~~
【分类号】：TP181;V267
本文编号：2509738
[Abstract]:In order to predict the working state of airborne equipment, a sparse kernel incremental learning machine (ELM) algorithm is proposed. In order to solve the problem of kernel matrix expansion in nuclear online learning, a two-step thinning method based on instantaneous information measurement is proposed, which combines construction and pruning strategy. By minimizing dictionary redundancy in the construction stage and maximizing the instantaneous conditional self-information of dictionary elements in the pruning stage, a sparse dictionary with fixed memory scale is selected. In order to solve the problem of kernel weight updating of incremental overlimited learning machines based on kernel, an improved sample reduction learning algorithm is proposed, which can update the inverse matrix of the residual kernel function Gram matrix after the deletion of any kernel function in the dictionary. Through the state prediction of a certain aircraft engine, under the condition that the predicted data length is equal to 20, the overall average error rate of the prediction is reduced to 2.18%. Compared with the on-line kernel overlimited learning machine algorithm of three manifolds, the prediction accuracy is improved by 0.72%, 0.14% and 0.13%, respectively.
【作者单位】： 海军航空工程学院科研部;
【基金】：国家自然科学基金(61571454)~~
【分类号】：TP181;V267
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1 雷达;基于智能学习模型的民航发动机健康状态预测研究[D];哈尔滨工业大学;2013年
，本文编号：2509738
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