TL;DR: A modified version of exponential cost function is proposed to improve the stability of adaptive algorithm, where the recursive algorithm is based on the Dawson function, resulting the SOV-ExRLS algorithm to achieve the improved performance in both α -stable and Gaussian environments.
Abstract: In this paper, we propose a modified version of exponential cost function to improve the stability of adaptive algorithm, where the recursive algorithm is based on the Dawson function. The second-order Volterra (SOV) filter is incorporated into the proposed recursive algorithm, resulting the SOV-ExRLS algorithm, to achieve the improved performance in both $$\alpha $$-stable and Gaussian environments. Moreover, the mean and mean-square behavior of the SOV-ExRLS algorithm is analyzed. In particular, the proposed cExRLS-IDLMS method is convexly combined with the functional link artificial neural network filter to flexibly model the chaotic memristor system. Simulation studies verify the analytical findings and reveal enhanced identification performance of the proposed algorithms over the existing nonlinear algorithms.
TL;DR: In this paper, a method for numerical evaluation of the weighted Hilbert transform over the entire real line, where the integral is taken in the Cauchy principal value sense, is presented.
TL;DR: A generalization of the modified Simpson's rule is derived and various error bounds for this generalization are established.
Abstract: A generalization of the modified Simpson's rule is derived. Various error bounds for this generalization are established. An application to Dawson integral is given.
TL;DR: By solving the differential equation d u / d y + 2 yu = 1 using the orthogonal rational Chebyshev functions of the second kind, SB 2 n ( y ; L ) , which generates a pentadiagonal Petrov–Galerkin matrix, one can obtain an accuracy of roughly N digits where N is the number of terms in the spectral series.
TL;DR: Numerical results show that the Lanczos τ-method can produce polynomial approximations as accurate as the truncated Faber series, with much less effort than is involved in computing the Faber coefficients.