A Novel Adaptive Nonlinear Filter-Based Pipelined Feedforward Second-Order Volterra Architecture
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.
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ABSTRACT: In this paper, an adaptive neuro-control structure for complex dynamic system is proposed. A recurrent Neural Network is trained-off-line to learn the inverse dynamics of the system from the observation of the input-output data. The direct adaptive approach is performed after the training process is achieved. A Lyapunov-Base training algorithm is proposed and used to adjust on-line the network weights so that the neural model output follows the desired one. The simulation results obtained verify the effectiveness of the proposed control method.Journal of Software Engineering and Applications 04/2012; 5:225-248.
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ABSTRACT: In this work, we present novel Bayesian algorithms for acoustic echo cancellation and residual echo suppression in the presence of a memoryless loudspeaker nonlinearity. The system nonlinearity is modeled using a basis-generic nonlinear expansion. This allows us to express the microphone observation in the DFT domain in terms of the nonlinear-expansion coefficients and the acoustic echo path. We augment the observation model with first-order Markov models for the echo-path vector and the nonlinear-expansion coefficients to arrive at a composite state-space model. The echo path vector and each nonlinear-expansion coefficient are designated as the unknown random variables in our Bayesian model. The posterior estimators for the random variables and the learning rules for the a priori unknown model parameters are then derived via the maximization of the variational lower bound on the log likelihood. We further show that a Bayesian post-filter for residual echo suppression can be derived by optimizing a minimum-mean-square error (MMSE) cost function subject to marginalization with respect to the posteriors estimated in the echo cancellation stage. The effectiveness of the approach is supported by simulation results and an analysis using instrumental performance measures.IEEE Transactions on Signal Processing 01/2013; 61(23):5853-5867. · 3.20 Impact Factor
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ABSTRACT: The electroencephalogram (EEG) is the most widely used method for diagnosis of brain diseases, where a good quality of recordings allows the proper interpretation and identification of physiological and pathological phenomena. However, EEG recordings are often contaminated by different kinds of noise. These annoying signals limit severely brain recording utility and, hence, have to be removed. To deal with this problem, in this paper an adaptive filtering framework is proposed for the enhancing of brain signal recordings. This new method is capable of reducing muscle and baseline noise in EEG signals with low EEG distortion and high noise cancellation. The advantages of the proposed method are demonstrated on real and synthetic brain signals with comparisons made to several benchmark methods. Results show that the proposed approach is preferable to the other systems by achieving a better trade-off between deleting noises and preserving inherent brain activities.Computers & Electrical Engineering 07/2013; 39(5):1561-1570. · 0.99 Impact Factor