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Recursive least mean p-power Extreme Learning Machine.
- Source :
-
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2017 Jul; Vol. 91, pp. 22-33. Date of Electronic Publication: 2017 Apr 12. - Publication Year :
- 2017
-
Abstract
- As real industrial processes have measurement samples with noises of different statistical characteristics and obtain the sample one by one usually, on-line sequential learning algorithms which can achieve better learning performance for systems with noises of various statistics are necessary. This paper proposes a new online Extreme Learning Machine (ELM, of Huang et al.) algorithm, namely recursive least mean p-power ELM (RLMP-ELM). In RLMP-ELM, a novel error criterion for cost function, namely the least mean p-power (LMP) error criterion, provides a mechanism to update the output weights sequentially. The LMP error criterion aims to minimize the mean p-power of the error that is the generalization of the mean square error criterion used in the ELM. The proposed on-line learning algorithm is able to provide on-line predictions of variables with noises of different statistics and obtains better performance than ELM and online sequential ELM (OS-ELM) while the non-Gaussian noises impact the processes. Simulations are reported to demonstrate the performance and effectiveness of the proposed methods.<br /> (Copyright © 2017 Elsevier Ltd. All rights reserved.)
- Subjects :
- Signal-To-Noise Ratio
Machine Learning standards
Subjects
Details
- Language :
- English
- ISSN :
- 1879-2782
- Volume :
- 91
- Database :
- MEDLINE
- Journal :
- Neural networks : the official journal of the International Neural Network Society
- Publication Type :
- Academic Journal
- Accession number :
- 28478371
- Full Text :
- https://doi.org/10.1016/j.neunet.2017.04.001