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The problem of identification of secondary path in active noise control applications is dealt with fundamentally using time-domain adaptive filters. The use of adaptive frequency domain subband identification as an alternative has some significant advantages which are overlooked in such applications. In this paper two different delayless subband adaptive algorithms for identification of an unknown...
A new family of adaptive structures which employ filter banks or wavelets to decompose the input signal and reduced-order adaptive filters in the subbands is applied to the acoustic echo control problem. Structures with sparse adaptive subfiltcrs and no down-sampling of the subband signals, as well as structures with critical sampling of the subband signals, arc investigated. Both types of structures...
Distributed control systems (DCSs) with intrinsic and coupling time delays in subsystems are studied, which also have nonlinearities satisfying generalized Lipschitz condition and some unknown parameters. Decoupling of states and parameters is emphasized. The distributed adaptive state observer is then designed for such system. Convergence of observer error dynamics and estimation of unknown parameters...
An repetitive learning control strategy is presented for a class of bilinear parametric systems with mixed unknown time-varying and time-invariant parameters and unknown time varying delay. By reconstructing the system equation, the time-varying delay is combined into an unknown periodic time-varying vector which is estimated by a periodic adaptive mechanism. The proposed control scheme includes a...
The recursive-least-squares (RLS) filters are known to be generally unsuitable for tracking time-varying systems. This paper proposes a scheme for improving the tracking performance of RLS filters by restarting them when estimation errors exceed a pre-specified value. This scheme is quite simple in essence and can be implemented with minimal amount of additional computation. Moreover it does not affect...
In this paper, the Multi-ADAptive LINear Element (MADA LINE) neural network was generalized for On-line System identification of linear time-invariant (LTI) Multi-Input Multi-Output (MIMO) systems. Based on the input output polynomial model which can be easily transformed into the row canonical state space model, Tapped delay line are introduced, so the MADALINE becomes recurrent in nature and thus...
In this paper, the problem of robust adaptive convergence for uncertain Cohen-Grossberg neural networks(CGNNs) with mixed delays is investigated. Using the Lyapunov method and employing a novel lemma, some delay-independent conditions are derived to ensure the state variables of the discussed robust system to converge, globally, uniformly, exponentially to a ball in the state space with a pre-specified...
In this paper, the problem of adaptive robust state observer design is considered for a class of uncertain nonlinear time-delay systems. It is supposed that the upper bound of the nonlinearity and uncertainty, including delayed states, is a linear function of some parameters which are still assumed to be unknown. An improved adaptation law with sigma-modification is employed to estimate the unknown...
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