Neural FIR adaptive Laguerre equalizer with a gradient adaptive amplitude for nonlinear channel in communication systems

To mitigate the linear and nonlinear distortions in communication systems, two novel nonlinear adaptive equalizers are proposed on the basis of the neural finite impulse response (FIR) filter, decision feedback architecture and the characteristic of the Laguerre filter. They are neural FIR adaptive decision feedback equalizer (SNNDFE) and neural FIR adaptive Laguerre equalizer (LSNN). Of these two equalizers, the latter is simple and with characteristics of both infinite impulse response (IIR) and FIR filte...     

Neural filtering method for stationary signal processes

In this paper a new method of neural filtering using artificial neural network systems is presented for the filtering problems of linear and nonlinear, stationary and nonstationary stochastic signals. The neural filter (denoted neurofilter) developed in this paper has either finite impulse response (FIR) structure or infinite impulse response (IIR) structure. The neurofilter differs from the conventional linear digital FIR and IIR filters because the artificial neural network system used in the neurofilter has a nonlinear structure due to the sigmoid function. Numerical studies for the estimation of a second-order Butterworth process are performed by changing the structures of the neurofilter in order to evaluate the performance indices under changes of the output noises or disturbances. The results obtained from these studies verified the capabilities which are essentially necessary for on-line filtering of various stochastic signals.     

Convergence analysis of recursive identification algorithms basedon the nonlinear Wiener model

Recursive identification algorithms, based on the nonlinear Wiener model, are presented. A recursive identification algorithm is first derived from a general parameterization of the Wiener model, using a stochastic approximation framework. Local and global convergence of this algorithm can be tied to the stability properties of an associated differential equation. Since inversion is not utilized, noninvertible static nonlinearities can be handled, which allows a treatment of, for example, saturating sensors and blind adaptation problems. Gauss-Newton and stochastic gradient algorithms for the situation where the static nonlinearity is known are then suggested in the single-input/single-output case. The proposed methods can outperform conventional linearizing inversion of the nonlinearity when measurement disturbances affect the output signal. For FIR (finite impulse response) models, it is also proved that global convergence of the schemes is tied to sector conditions on the static nonlinearity. In particular, global convergence of the stochastic gradient method is obtained, provided that the nonlinearity is strictly monotone. The local analysis, performed for IIR (infinite impulse response) models, illustrates the importance of the amplitude contents of the exciting signals     

A Constructive Approach to Approximate Linear Periodic Systems

In this note, the problem on approximating a linear periodic system with period N by a periodic model with period M(0infin norms. Under this framework, both finite impulse response (FIR) and infinite impulse response (IIR) periodic systems can be efficiently approximated by using a constructive linear matrix inequality approach. Finally, some numerical examples are given to illustrate the effectiveness of the proposed approach     

Elimination of noise on transcranial Doppler signal using IIR filters designed with artificial bee colony – ABC-algorithm

Biomedical signals are usually contaminated by noise generated from sources such as power line interference and disturbances produced by the movement of the recording electrodes. Also the signal-to-noise ratio of biomedical signals is usually quite low. In addition, biomedical signals often interfere with each other. Therefore, the filters employed for eliminating noise and interference are significant in the medical practice. Digital infinite impulse response (IIR) filters have shorter filter length than the finite impulse response (FIR) filters with the same frequency characteristic. Therefore, in this work, an approach based on digital IIR filters are described for the elimination of noise on transcranial Doppler by using artificial bee colony (ABC) which is a popular swarm based optimization algorithm introduced recently. Moreover, the performance of the proposed approach is compared to particle swarm optimization algorithm.     

An alternative FIR filter for state estimation in discrete-time systems

To overcome the resulting problems of existing finite impulse response (FIR) structure filters, this paper proposes an alternative FIR filter for state estimation in discrete-time systems, which is derived from the well-known Kalman filter with recursive infinite impulse response (IIR) structure. The proposed FIR filter obtains a posteriori knowledge about the window initial condition from the most recent finite observations, while existing FIR filters handle this task arbitrarily or heuristically. The gain matrix for the proposed FIR filter incorporates a posteriori knowledge about the window initial condition during its design and is shown to be time-invariant. The proposed FIR filter is shown to have good inherent properties such as unbiasedness and deadbeat. Through extensive computer simulations, the proposed FIR filter can be shown to be comparable with the Kalman filter for the nominal system and better than that for the temporarily uncertain system.     

有功电能计量IP核的设计

对有功电能计量的数学模型进行了分析,给出了相应的IP核实现模型,并详细讨论了CIC抽取滤波器、IIR高通滤波器、FIR低通滤波器、数字频率变换等模块的原理与设计。利用Simulink模型进行了仿真,用VHDL作为设计语言,在QuartusⅡ软件下完成综合和仿真,并在Altera公司的FPGA芯片CycloneⅡEP2C35F484C8目标板上实现设计。     

Process identification using finite impulse response models : A solution to the 1992 Canadian chemical engineering conference process identification workshop problem

The process model identification problem described by Cott was solved using Dynamic Matrix Identification (DMI), a commercial software package that produces finite impulse response (FIR) models. Unique aspects of FIR models and the results of the workshop problem are discussed.     

IIR system identification using cat swarm optimization

Conventional derivative based learning rule poses stability problem when used in adaptive identification of infinite impulse response (IIR) systems. In addition the performance of these methods substantially deteriorates when reduced order adaptive models are used for such identification. In this paper the IIR system identification task is formulated as an optimization problem and a recently introduced cat swarm optimization (CSO) is used to develop a new population based learning rule for the model. Both actual and reduced order identification of few benchmarked IIR plants is carried out through simulation study. The results demonstrate superior identification performance of the new method compared to that achieved by genetic algorithm (GA) and particle swarm optimization (PSO) based identification.     

FIR filters and recursive forms for continuous time-invariant state-space models

An FIR (finite impulse response) filter and an FIR smoother are introduced for continuous time-invariant state-space models. It is shown in this note that finite impulse responses of the FIR filter and smoother can be easily determined by solving a simple Riccati-type matrix differential equation on a finite interval. Especially for systems with stationary processes, finite impulse responses of the FIR filter and smoother become time-invariant and can be computed from simpler equations. For fast computational purposes, recursive forms of the FIR filter and smoother are derived by using adjoint variables. In this case all gains for recursive forms are shown to be constant.     

L2−E FIR Smoothers for Deterministic Discrete-Time State–Space Signal Models

In this note, a new type of L₂ - E performance criterion for a fixed-lag smoother is introduced, which is given by a gain between the energy of the external disturbances during the recent time horizon and the estimation error at the fixed-delayed time from the current one. By minimizing the maximum value of the L₂ - E performance criterion, the L₂-E finite impulse response (FIR) smoother(LEFS) is obtained for a deterministic discrete-time state space signal model. The LETS is designed to meet requirements such as linearity and FIR structure with respect to measured inputs and outputs, unbiased property in the deterministic sense, and independence of any state information simultaneously. The proposed LETS is represented in a batch form for simplicity. The LETS is compared with a conventional H _infin Infinite Impulse Response (IIR) smoother via simulation     

Jackknife and bootstrap methods in the identification of dynamic models

A new criterion based on a Jackknife or a Bootstrap statistic is proposed for identifying non-parsimonious dynamic models (FIR, ARX). It is applicable for selecting the number of components in latent variable regression methods or the constraining parameter in regularized least squares regression methods. These meta parameters are used to overcome ill-conditioning caused by model over-parameterization, when fitted using prediction error or least squares methods. In all cases studied, using PLS for parameter estimation, the proposed criterion led to the selection of better models, in the mean square error sense, than when selected via cross-validation. The methodology also provides approximate confidence intervals for the model parameters and the step and impulse response of the system.     

A novel algorithm for the design of selective FIR filters with arbitrary amplitude and phase characteristics

In this paper, a novel analytic technique is presented for the design of finite impulse response digital filters with simultaneous amplitude and phase requirements. The design process relies on the interpolation concept and is based on the generation of arbitrary amplitude and phase polynomials. The new class of FIR filters lays in between the exact linear phase filters at all frequencies and the minimum phase FIR filters based on the amplitude approximation only. It interpolates arbitrary (e.g., linear) phase response in the passband and arbitrary amplitude characteristics in pass- and stopbands. The introduced class of filters can be used on the amplitude and phase equalization of both FIR and IIR digital filters. Illustrative examples are included in order to investigate the main properties of these filters and to demonstrate that the proposed design method leads to a significant saving in the arithmetic computations.     

DMC鲁棒稳定性分析

以脉冲响应模型(FIR)描述系统,采用脉冲响应系数误差平方和的形式定义了模型的不 确定性,分析得到了基于脉冲响应模型的动态矩阵控制(DMC)算法的闭环系统,在此基础上,推 导得出了DMC闭环系统的鲁棒稳定性条件.     

Closed-form unbiased frequency estimation of a noisy sinusoid using notch filters

In this note, the problem of the frequency estimation of a sinusoid embedded in white noise is considered. The approach used herein is the minimization of the sample variance of the output of constrained notch filters fed by the noisy sinusoid. In particular, this note focuses on closed-form expressions of the frequency estimate, which can be obtained using notch filters having an all-zeros finite-impulse response (FIR) structure. The results presented in this note are as follows: 1) it is shown that the FIR notch filters obtained from standard second-order infinite-impulse response (IIR) filters are inadequate; 2) a new second-order IIR notch filter is proposed, which provides an unbiased estimate of the frequency; 3) the FIR filter obtained from the new IIR filter provides a closed-form unbiased frequency estimate; and 4) the closed-form frequency estimate obtained using the new FIR notch filter asymptotically converges toward the Pisarenko harmonic decomposition estimator and the Yule-Walker estimator.     

Minimax FIR smoothers for deterministic continuous-time state space models

In order to design a smoother for a deterministic continuous-time state space model, a new performance criterion is proposed, which is given by a ratio of the current estimation error to the weighted energy of the deterministic disturbance applied during the recent finite horizon. Among smoothers with the deadbeat property and finite impulse response (FIR) structure, a minimax FIR smoother (MFS) is obtained to optimize the proposed performance criterion. To begin with, the functional optimization problem is formulated with respect to kernel functions of the MFS and then its solution is explicitly presented. The MFS depends only on inputs and outputs on the finite recent horizon, and is independent of any a priori state information. The MFS is first represented in an integral form for simple representation and then a differential form is introduced for efficient numerical computation. As in H_∞ IIR smoothing and H₂ IIR filtering, it is shown that the proposed MFS for a deterministic system can be interpreted as the minimum variance unbiased FIR smoother for a stochastic system.     

含正反馈振动主动控制系统的混合自适应控制

对于存在结构正反馈的振动主动控制系统,传统的基于有限冲击响应的自适应前馈控制器设计方法难以同时保证控制系统稳定与良好的控制性能.本文在分析正反馈对前馈控制系统影响的基础上,基于无限冲击响应控制器设计模式,提出一种结合前馈自适应控制器和反馈自适应控制器的混合自适应振动主动控制方法.其中前馈自适应控制器采用参考传感器采集到的扰动相关信号作为参考信号,反馈自适应控制器通过构建扰动的估计量作为参考信号,控制器参数更新采用Landau参数递推算法.以一典型的具有固有正反馈性质的机械振动系统为控制对象,给出了该混合自适应控制算法的详细推导过程以及稳定性和收敛性分析过程,得到了算法稳定与收敛的严格正实条件以及相应放松严格正实条件的要求.在此基础上,通过构建实时振动主动控制实验平台,针对多种振动扰动开展对比实验分析.相关实验结果验证了本文提出的混合自适应振动主动控制方法的可行性和有效性.     

Adaptive deconvolution and identification of nonminimum phase FIRsystems based on cumulants

A novel adaptive deconvolution and system identification scheme is introduced for a linear, non-minimum-phase finite-impulse-response (FIR) system driven by non-Gaussian white noise. The adaptive scheme is based on approximating the FIR system by noncausal autoregressive (AR) models and using higher order cumulants of the system output. As such, it is a blind equalization (deconvolution) scheme. The set of updated AR parameters is obtained by using a gradient-type algorithm and by using higher order cumulants instead of time samples of the output signal. It is demonstrated by means of extensive simulations that the adaptive scheme works well for both stationary and nonstationary cases. As expected, it outperforms the autocorrelation-based gradient method for nonminimum-phase system identification and deconvolution. Performance comparisons to existing methods are given, using as figures of merit the probability of errors in the restored input sequence, computational complexity, and convergence rate     

Identification of linear systems using polynomial kernels in the frequency domain

In prior work we presented an identification algorithm using polynomials in the time domain. In this article, we extend this algorithm to include polynomials in the frequency domain. A polynomial is used to represent the imaginary part of the Fourier transform of the impulse response. The Hilbert transform relationship is used to compute the real part of the frequency response and hence the complete process model. The polynomial parameters are computed based on the computationally efficient linear least square method. The order of the polynomial is estimated based on residue decrement. Simulated and experimental results show the effectiveness of this method, particularly for short input/output data sequence with high signal to noise ratio. The frequency domain polynomial model complements the time domain methods since it can provide a good estimate of the time to steady state for time domain FIR (finite impulse response) models. Confidence limits in time or frequency domain can be computed using this approach. Noise rejection properties of the algorithm are illustrated using data from both simulated and real processes.