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Indexed by:期刊论文

Date of Publication:2011-11-01

Journal:Zidonghua Xuebao/Acta Automatica Sinica

Included Journals:EI、PKU、ISTIC、Scopus

Volume:37

Issue:11

Page Number:1344-1350

ISSN No.:02544156

Abstract:Focusing on the ill-posed problem and the model scale control of ELM (Extreme learning machine), this paper proposes an improved ELM algorithm based on 1-norm regularization term. This is achieved by involving an 1-norm regularization term into the original square cost function, and it can be used to control the model scale and enhance the generalization capability. Furthermore, to simplify the solving process of the 1-norm regularization method, the bound optimization algorithm is employed and a suitable surrogate function is established. Based on the surrogate function, the Bayesian algorithm can be used to substitute the complicated cross validation method and estimate the regularization parameter adaptively. Simulation results illustrate that the proposed method can effectively simplify the model structure, while remaining acceptable prediction accurate. Copyright ? 2011 Acta Automatica Sinica. All rights reserved.