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In this paper, a novel active noise control (ANC) scheme based on neural networks is presented for nonlinear ANC systems without the identification of secondary path by introducing virtual primary noises. The ANC system is analyzed in the form of discrete-time state equations. The proposed controller employs neural networks to attenuate the noises. The proposed scheme does not require the dynamical...
Wavelet neural network (WNN) trained by unscented Kalman filter (UKF) has many merits of fast convergent rate and small prediction error without computing the Jacobian matrix. Based on this, an improved UKF is introduced into the parameters estimation for WNN. The algorithm uses an unscented transform (UT) based on minimal skew simplex Sigma point sampling strategy in the frame of Kalman filter, which...
This paper proposed a new direct nonlinear controller design method based on virtual reference(VR) and support vector machine(SVM), which allows to directly design nonlinear controller on the base of input/output data with no need of a model of the plant. Firstly, the relation between virtual reference feedback tuning(VRFT) and internal model control(IMC) was analyzed. Then, the structure and design...
This paper proposes a new narrowband active noise control (ANC) system where an ANFIS (adaptive network-based fuzzy inference system) is utilized as an adaptive controller. The ANFIS is a combination of a neural network and a fuzzy inference system. For the purpose of computational cost reduction, the nonlinear premise parameters in the ANFIS are fixed and only its linear consequent parameters are...
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