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- W2079082914 abstract "In this paper, a novel adaptive control scheme is investigated based on the backstepping design for a class of stochastic nonlinear systems with unmodeled dynamics and time-varying state delays. The radial basis function neural networks are used to approximate the unknown nonlinear functions obtained by using Ito differential formula and Young׳s inequality. The unknown time-varying delays and the unmodeled dynamics are dealt with by constructing appropriate Lyapunov–Krasovskii functions and introducing available dynamic signal. It is proved that all signals in the closed-loop system are bounded in probability and the error signals are semi-globally uniformly ultimately bounded (SGUUB) in mean square or the sense of four-moment. Simulation results illustrate the effectiveness of the proposed design." @default.
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- W2079082914 date "2014-06-01" @default.
- W2079082914 modified "2023-10-01" @default.
- W2079082914 title "Adaptive neural control of stochastic nonlinear systems with unmodeled dynamics and time-varying state delays" @default.
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- W2079082914 doi "https://doi.org/10.1016/j.jfranklin.2014.02.013" @default.
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