Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/101013
Title: Recurrent online kernel recursive least square algorithm for nonlinear modeling
Authors: Fan, Haijin
Song, Qing
Xu, Zhao
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Fan, H., Song, Q., & Xu, Z. (2012). Recurrent online kernel recursive least square algorithm for nonlinear modeling. IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, pp.1574-1579.
Abstract: In this paper, we proposed a recurrent kernel recursive least square (RLS) algorithm for online learning. In classical kernel methods, the kernel function number grows as the number of training sample increases, which makes the computational cost of the algorithm very high and only applicable for offline learning. In order to make the kernel methods suitable for online learning where the system is updated when a new training sample is obtained, a compact dictionary (support vectors set) should be chosen to represent the whole training data, which in turn reduces the number of kernel functions. For this purpose, a sparsification method based on the Hessian matrix of the loss function is applied to continuously examine the importance of the new training sample and determine the update of the dictionary according to the importance measure. We show that the Hessian matrix is equivalent to the correlation matrix of the training samples in the RLS algorithm. This makes the sparsification method able to be easily incorporated into the RLS algorithm and reduce the computational cost futher. Simulation results show that our algorithm is an effective learning method for online chaotic signal prediction and nonlinear system identification.
URI: https://hdl.handle.net/10356/101013
http://hdl.handle.net/10220/16315
DOI: 10.1109/IECON.2012.6388534
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

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