Journal ArticleDOI
A Novel Adaptive Nonlinear Filter-Based Pipelined Feedforward Second-Order Volterra Architecture
Haiquan Zhao,Jiashu Zhang +1 more
TLDR
Compared with the conventional SOV filter, adaptive JPPSOV filter exhibits a better convergence performance with less computational burden in terms of convergence speed and steady-state error.Abstract:
Due to the computational complexity of the Volterra filter, there are limitations on the implementation in practice. In this paper, a novel adaptive joint process filter using pipelined feedforward second-order Volterra architecture (JPPSOV) to reduce the computational burdens of the Volterra filter is proposed. The proposed architecture consists of two subsections: nonlinear subsection performing a nonlinear mapping from the input space to an intermediate space by the feedforward second-order Volterra (SOV), and a linear combiner performing a linear mapping from the intermediate space to the output space. The corresponding adaptive algorithms are deduced for the nonlinear and linear combiner subsections, respectively. Moreover, the analysis of theory shows that these adaptive algorithms based on the pipelined architecture are stable and convergence under a certain condition. To evaluate the performance of the JPPSOV, a series of simulation experiments are presented including nonlinear system identification and predicting of speech signals. Compared with the conventional SOV filter, adaptive JPPSOV filter exhibits a litter better convergence performance with less computational burden in terms of convergence speed and steady-state error.read more
Citations
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Journal ArticleDOI
Hybrid time-frequency domain equalization for LED nonlinearity mitigation in OFDM-based VLC systems
TL;DR: A novel hybrid time-frequency domain equalization scheme is proposed and experimentally demonstrated to mitigate the white light emitting diode (LED) nonlinearity in visible light communication systems based on orthogonal frequency division multiplexing (OFDM).
Journal ArticleDOI
A Comprehensive Approach to Universal Piecewise Nonlinear Regression Based on Trees
TL;DR: In this paper, a tree-based piecewise linear regression algorithm for adaptive nonlinear regression is proposed. But the algorithm does not directly minimize the final regression error, which is the ultimate performance goal.
Journal ArticleDOI
Low-Complexity Nonlinear Adaptive Filter Based on a Pipelined Bilinear Recurrent Neural Network
TL;DR: A novel low-complexity nonlinear adaptive filter with a pipelined bilinear recurrent neural network (PBLRNN) is presented in this paper and shows considerably better performance compared to the single BLRNN and RNN models.
Journal ArticleDOI
Recursive Adaptive Sparse Exponential Functional Link Neural Network for Nonlinear AEC in Impulsive Noise Environment
Sheng Zhang,Wei Xing Zheng +1 more
TL;DR: A recursive adaptive sparse exponential TFLN based on sparse representations of functional links is developed, and the robust proportionate adaptive algorithm is deduced from the robust cost function over the RASETFLN in impulsive noise environments.
Journal ArticleDOI
A study about Chebyshev nonlinear filters
TL;DR: It is shown in the paper that the perfect periodic sequences of Chebyshev nonlinear filters are simply related to those of even mirror Fourier nonlinear systems, and can be identified with the cross-correlation method.
References
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Book
Adaptive Filter Theory
TL;DR: In this paper, the authors propose a recursive least square adaptive filter (RLF) based on the Kalman filter, which is used as the unifying base for RLS Filters.
Journal ArticleDOI
Identification and control of dynamical systems using neural networks
TL;DR: It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems and the models introduced are practically feasible.
Book
The Volterra and Wiener Theories of Nonlinear Systems
TL;DR: In this article, a complete and detailed development of the analysis, design and characterization of non-linear systems using the Volterra and Wiener theories, as well as gate functions, is presented.
Journal ArticleDOI
On the "Identification and control of dynamical systems using neural networks"
E. Rios-Patron,Richard D. Braatz +1 more
TL;DR: Referring to the above said paper by Narendra-Parthasarathy (ibid.
Book
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
TL;DR: This book shows researchers how recurrent neural networks can be implemented to expand the range of traditional signal processing techniques.