一种基于积累一致性测量的在线状态预测算法

doi:10.16183/j.cnki.jsjtu.2017.11.016

摘要/Abstract

摘要： 针对核极限学习机（Extreme Learning Machine with Kernel, KELM）在线应用过程中，核矩阵膨胀，导致算法复杂性不断上升，且难以跟踪系统时变特征的问题，以滑动时间窗为基本建模策略，提出了一种新的KELM在线稀疏学习算法.在前向与后向稀疏化过程中，基于提出的构造与修剪策略，通过在线最小化字典的积累一致性，可选择一组具有预定规模的关键节点.在增样学习与减样学习过程中，基于节点选择结果，利用矩阵的初等变换与分块矩阵求逆公式，模型参数能被在线递推更新.提出的算法被用于混沌时间序列预测与音频放大器状态预测.实验结果表明：相比于4种流形的在线序贯ELM算法，提出的方法在花费相似的测试时间的条件下，能够显著提升预测精度，且具有较好的稳定性.

关键词: 状态预测, 核方法, 极限学习机, 积累一致性, 在线序贯学习

Abstract: It is difficult for extreme learning machine with kernel (KELM) to curb kernel matrix expansion and track the system dynamic changes effectively when it is applied to solve online learning tasks. So the sliding time window method is regarded as the basic modeling strategy, and a new online sparsification learning algorithm for KELM is proposed in this paper. In the process of forward sparsification and backward sparsification, a sparse dictionary with predefined size can be selected by online minimization of its cumulative coherence based on our proposed constructive and pruning strategy. In the process of incremental learning and decremental learning, the model parameters can be directly updated by elementary transformation of matrices and block matrix inversion formula based on the selected dictionary. The performance of the proposed algorithm is compared with several well-known online sequential ELM algorithms. The simulation results show that the proposed algorithm can achieve higher prediction accuracy and better stability, meanwhile, it costs the similar testing time.

Key words: condition prediction, kernel method, extreme learning machine (ELM), cumulative coherence, online sequential learning

中图分类号:

张伟，许爱强，高明哲. 一种基于积累一致性测量的在线状态预测算法[J]. 上海交通大学学报（自然版）, 2017, 51(11): 1391-1398.

ZHANG Wei，XU Aiqiang，GAO Mingzhe. An Online Condition Prediction Algorithm Based on Cumulative Coherence Measurement [J]. Journal of Shanghai Jiaotong University, 2017, 51(11): 1391-1398.

参考文献

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