Topic
Volterra series
About: Volterra series is a research topic. Over the lifetime, 2731 publications have been published within this topic receiving 46199 citations.
Papers published on a yearly basis
Papers
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17 Sep 1992
TL;DR: An algorithm based on recursive approximation and estimation is proposed for the identification of nonlinear systems which can be modeled by a sparse Volterra series and can be directly applied to the delay estimation of a sparse finite impulse response (FIR) filter.
Abstract: An algorithm based on recursive approximation and estimation is proposed for the identification of nonlinear systems which can be modeled by a sparse Volterra series. The algorithm detects the terms of the Volterra series on which the output depends and estimates the associated Volterra kernels using a least squares criterion. The performance of the algorithm is primarily dependent on the number of nonzero Volterra kernels and not on their distribution in the whole series. The input sequence can be either i.i.d. or correlated. The algorithm can also be directly applied to the delay estimation of a sparse finite impulse response (FIR) filter. >
23 citations
07 May 1996
TL;DR: An adaptive nonlinear filter based on a second order Volterra series and on an IIR filter structure is presented, able to model higher than second order nonlinearities for systems where the non linearities are harmonically related.
Abstract: An adaptive nonlinear filter based on a second order Volterra series and on an IIR filter structure is presented. This filter is able to model higher than second order nonlinearities for systems where the nonlinearities are harmonically related. This solution represents an alternative to using higher than second order Volterra filters. We present a full derivation of this gradient search based adaptive nonlinear filter and also highlight the various assumptions and simplifications which require to be made in order to produce a practical algorithm. A comparison is made in terms of the performance and computational complexity between an adaptive second order IIR Volterra filter and an adaptive second and third order Volterra filters.
23 citations
TL;DR: This paper introduces a novel algorithm for morphing any accelerogram into a spectrum matching one, and the weighting of every term in this Volterra series is optimally adapted using a Levenberg–Marquardt algorithm such that the modified time series matches any target response spectrum.
Abstract: In this paper, we introduce a novel algorithm for morphing any accelerogram into a spectrum matching one First, the seed time series is re-expressed as a discrete Volterra series The first-order Volterra kernel is estimated by a multilevel wavelet decomposition using the stationary wavelet transform Second, the higher-order Volterra kernels are estimated using a complete multinomial mixing of the first-order kernel functions Finally, the weighting of every term in this Volterra series is optimally adapted using a Levenberg–Marquardt algorithm such that the modified time series matches any target response spectrum Comparisons are made using the SeismoMatch algorithm, and this reweighted Volterra series algorithm is demonstrated to be considerably more robust,matching the target spectrum more faithfully This is achieved while qualitatively maintaining the original signal’s non-stationary statistics, such as general envelope, time location of large pulses, and variation of frequency content with time
23 citations
25 May 2003
TL;DR: A 30 W LDMOS is modeled using a 5th order polynomial model and compared to the large-signal MET model using harmonic balance to find out the dominant cause of distortion for a class A biased amplifier.
Abstract: A 30 W LDMOS is modeled using a 5th order polynomial model. The polynomial model is compared to the large-signal MET model using harmonic balance, and as the results agreed very well, the polynomial model was imported to a numerical Volterra simulator to find out the dominant cause of distortion for a class A biased amplifier. The characterization technique is briefly discussed.
23 citations
TL;DR: In this paper, the authors describe different solutions for behavioural identification of non-linear dynamic systems, all based on a modified Volterra series, which is characterized by a reduced number of operators with respect to the classical VOLTERRA approach, allowing a reliable extraction of the model parameters by means of conventional instrumentation, without the need for the generation of complicated input probing signals or additional approximations.
23 citations