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Real-time identified chaotic plants using neural enhanced learning machine technique

Ho Pham Huy Anh (Faculty of Electrical and Electronics Engineering (FEEE), Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam and Vietnam National University of Ho Chi Minh City (VNU-HCM), Ho Chi Minh City, Vietnam)

Engineering Computations

ISSN: 0264-4401

Article publication date: 8 January 2021

Issue publication date: 9 July 2021




This paper aims to propose a new neural-based enhanced extreme learning machine (EELM) algorithm, used as an online adaptive estimation model, regarding undetermined system dynamics and containing internal/external perturbations.


The EELM structure bases on the single layer feed-forward neural (SLFN) model in which the hidden weighting coefficients are initiated in random and the weighting outputs of the SLFN are online modified using an online adaptive rule implemented from Lyapunov stability concept.


Four different benchmark uncertain chaotic system tests have been satisfactorily investigated for demonstrating the superiority of proposed EELM technique.


Authors confirm that this manuscript is original.



We acknowledge the support of time and facilities from Ho Chi Minh City University of Technology (HCMUT) and VNU-HCM for this study.


Anh, H.P.H. (2021), "Real-time identified chaotic plants using neural enhanced learning machine technique", Engineering Computations, Vol. 38 No. 6, pp. 2810-2832.



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