This paper develops a statistical signal processing algorithm for parameter estimation of Euler-Bernoulli beams from limited and noisy measurement. The original problem is split into two reduced-order sub-problems coupled by a linear equation. The first sub-problem is cast as an inverse problem and solved by using Bayesian approximation error analysis. The second sub-problem is cast as a forward problem and solved by using the finite element technique. An optimal solution to the original problem is then obtained by coupling the solutions to the two sub-problems. Finally, a statistical signal processing algorithm for adaptive estimation of the optimal solution is developed. Computer simulation shows the effectiveness of the proposed algorithm.
This paper proposes a novel adaptive active noise control algorithm based on Tikhonov regularization theory. A regularized cost function consisting of the weighted sum of the most recent samples of the residual noise and its derivative is defined. By setting the gradient vector of the cost function to zero, an optimal solution for the control parameters is obtained. Based on the proposed optimal solution, a computationally efficient algorithm for adaptive adjustment of the control parameters is developed. It is shown that the regularized affine projection algorithm can be considered as a very special case of the proposed algorithm. Different computer simulation experiments show the validity and efficiency of the proposed algorithm.
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