A novel adaptive bilinear filter based on pipelined architecture

Authors:
Jiashu Zhang;Haiquan Zhao
Affiliations:
Si-Chuan Province Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu 610031, China;Si-Chuan Province Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu 610031, China
Venue:
Digital Signal Processing
Year:
2010

Citing 11
Cited 5

An efficient approximation to the quadratic Volterra filter and its application in real-time loudspeaker linearization

Signal Processing
The Volterra and Wiener Theories of Nonlinear Systems

The Volterra and Wiener Theories of Nonlinear Systems
Adaptive Filtering: Algorithms and Practical Implementation

Adaptive Filtering: Algorithms and Practical Implementation
Adaptive lattice bilinear filters

IEEE Transactions on Signal Processing
Nonlinear adaptive prediction of complex-valued signals by complex-valued PRNN

IEEE Transactions on Signal Processing
Nonlinear adaptive prediction of speech with a pipelined recurrentneural network

IEEE Transactions on Signal Processing
Nonlinear adaptive prediction of nonstationary signals

IEEE Transactions on Signal Processing
Adaptive parallel-cascade truncated Volterra filters

IEEE Transactions on Signal Processing
Parallel-cascade realizations and approximations of truncatedVolterra systems

IEEE Transactions on Signal Processing
Minimum Mean-Square Error Equalization for Second-Order Volterra Systems

IEEE Transactions on Signal Processing - Part I
Equivalent system model and equalization of differential impulse radio UWB systems

IEEE Journal on Selected Areas in Communications

Neural networks letter: A novel joint-processing adaptive nonlinear equalizer using a modular recurrent neural network for chaotic communication systems

Neural Networks
Volterra system-based neural network modeling by particle swarm optimization approach

Neurocomputing
Differential evolution-based nonlinear system modeling using a bilinear series model

Applied Soft Computing
Volterra filter modeling of nonlinear discrete-time system using improved particle swarm optimization

Digital Signal Processing
Volterra kernel based face recognition using artificial bee colonyoptimization

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

To overcome the computational complexity of the Volterra filter, a novel adaptive joint process filter using pipelined bilinear polynomial architecture (JPBPF) is proposed in this paper. The proposed architecture consists of two subsections: nonlinear subsection performing a nonlinear mapping from the input space to an intermediate space by the bilinear polynomial filter (BPF), and a linear filter performing a linear mapping from the intermediate space to the output space. The corresponding adaptive algorithms are deduced for the nonlinear subsection and linear filter subsection, respectively. To evaluate the performance of the JPBPF, a series of simulations are presented including nonlinear system identification, predicting of speech signals and nonlinear channel equalization. Compared with the conventional second-order Volterra (SOV) filter and BPF, the JPBPF exhibits a slightly better convergence performance in terms of convergence speed and steady-state error. Moreover, since those modules of a JPBPF can be performed simultaneously in a pipelined parallelism fashion, this would lead to a significant improvement in its total computational efficiency.