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Adaptive online estimation of hyper-chaotic plants using prescribed performance neural learning approach.

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Title: Adaptive online estimation of hyper-chaotic plants using prescribed performance neural learning approach.
Authors: Anh, Ho Pham Huy1,2 (AUTHOR) hphanh@hcmut.edu.vn, Dat, Nguyen Tien1,2 (AUTHOR) ntdat@hcmut.edu.vn
Source: Neural Computing & Applications. Jun2026, Vol. 38 Issue 11, p1-24. 24p.
Abstract: This study introduces a novel neural estimation method ensuring prescribed performance enabling the online identification of uncertain chaotic plants through which the transient-time and state residuals separately adjusted. The Lyapunov stability concept is applied for guaranteeing the stability in adaptive real-time system identification and control process. Outstanding from precedent researches, both identified scheme and training procedure along with control laws are initiatively designed as to permit separately adjusting the transient-time value from erroneous residual one. Then the real-time estimation process is to be satisfactorily investigated, in which the uncertain parts are parameterized using neural-based learning models, whose parameterized procedure helps efficiently solving more complicated problems, including adaptive observer and advanced adaptive neural-based control approaches. Here hyper-chaotic plants are comprehensively tested as to confirm the superiority and adaptability of proposed algorithm in comparison with other recently published advanced estimation approaches. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:This study introduces a novel neural estimation method ensuring prescribed performance enabling the online identification of uncertain chaotic plants through which the transient-time and state residuals separately adjusted. The Lyapunov stability concept is applied for guaranteeing the stability in adaptive real-time system identification and control process. Outstanding from precedent researches, both identified scheme and training procedure along with control laws are initiatively designed as to permit separately adjusting the transient-time value from erroneous residual one. Then the real-time estimation process is to be satisfactorily investigated, in which the uncertain parts are parameterized using neural-based learning models, whose parameterized procedure helps efficiently solving more complicated problems, including adaptive observer and advanced adaptive neural-based control approaches. Here hyper-chaotic plants are comprehensively tested as to confirm the superiority and adaptability of proposed algorithm in comparison with other recently published advanced estimation approaches. [ABSTRACT FROM AUTHOR]
ISSN:09410643
DOI:10.1007/s00521-026-12152-6