Performance analysis of multi-innovation gradient type identification methods
Performance analysis of multi-innovation gradient type identification methods
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DOI：
10.1016/j.automatica.2006.07.024
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发表时间：
2007-01-01
期刊：
影响因子：
6.4
通讯作者：
Chen, Tongwen
中科院分区：
文献类型：
--
作者：
Ding, Feng;Chen, Tongwen
随机梯度（SG）辨识算法收敛速度慢。为了提高算法的收敛速度，从新息修正的角度对SG算法进行了扩展，提出了多新息梯度型辨识算法，包括多新息随机梯度（MISG）算法和多新息遗忘梯度（MIFG）算法.由于多新息梯度型算法在每次迭代时不仅使用当前数据，而且还使用过去数据，因此可以提高参数估计精度。最后，性能分析和仿真结果表明，所提出的MISG和MIFG算法具有更快的收敛速度和更好的跟踪性能比相应的SG算法。(c)2006爱思唯尔有限公司保留所有权利。
It is well-known that the stochastic gradient (SG) identification algorithm has poor convergence rate. In order to improve the convergence rate, we extend the SG algorithm from the viewpoint of innovation modification and present multi-innovation gradient type identification algorithms, including a multi-innovation stochastic gradient (MISG) algorithm and a multi-innovation forgetting gradient (MIFG) algorithm. Because the multi-innovation gradient type algorithms use not only the current data but also the past data at each iteration, parameter estimation accuracy can be improved. Finally, the performance analysis and simulation results show that the proposed MISG and MIFG algorithms have faster convergence rates and better tracking performance than their corresponding SG algorithms. (c) 2006 Elsevier Ltd. All rights reserved.