Nonlinear feedback active noise control for broadband chaotic noise

Feedback active noise control has been used for tonal noise only and it is impractical for broadband noise. In this paper, it has been proposed that the feedback ANC algorithm can be applied to a broadband noise if the noise characteristic is chaotic in nature. Chaotic noise is neither tonal nor random; it is broadband and nonlinearly predictable. It is generated from dynamic sources such as fans, airfoils, etc. Therefore, a nonlinear controller using a functional link artificial neural network is proposed in a feedback configuration to control chaotic noise. A series of synthetic chaotic noise is generated for performance evaluation of the algorithm. It is shown that the proposed nonlinear controller is capable to control the broadband chaotic noise using feedback ANC which uses only one microphone whereas the conventional filtered-X least mean square (FXLMS) algorithm is incapable for controlling this type of noise.     

Particle swarm optimization based nonlinear active noise control under saturation nonlinearity

In this paper, a method is proposed to overcome the saturation non-linearity linked to the microphones and loudspeakers of active noise control (ANC) system. The reference microphone gets saturated when the acoustic noise at the source increases beyond the dynamic limits of the microphone. When the controller tries to drive the loudspeaker system beyond its dynamic limits, the saturation nonlinearity is also introduced into the system. The secondary path which is generally estimated with a low level auxiliary noise by a linear transfer function does not model such saturation nonlinearity. Therefore, the filtered-x least mean square (FXLMS) algorithm fails to perform when the noise level is increased. For alleviating the saturation nonlinearity effect a nonlinear functional expansion based ANC algorithm is proposed where the particle swarm optimization (PSO) algorithm is suitably applied to tune the parameters of a filter bank based functional link artificial neural network (FLANN) structure, named as PSO based nonlinear structure (PSO-NLS) algorithm. The proposed algorithm does not require any computation of secondary path estimate filtering unlike other conventional gradient based algorithms and hence has got computational advantage. The computer simulation experiments show its superior performance compared to the FXLMS, filtered-s LMS and genetic algorithms under saturation present at both at secondary and reference paths. The paper also includes a sensitivity analysis to study the effect of different parameters on ANC performance.     

Improved functional link artificial neural network via convex combination for nonlinear active noise control

A method relying on the convex combination of two normalized filtered-s least mean square algorithms (CNFSLMS) is presented for nonlinear active noise control (ANC) systems with a linear secondary path (LSP) and nonlinear secondary path (NSP) in this paper. The proposed CNFSLMS algorithm-based functional link artificial neural network (FLANN) filter, aiming to overcome the compromise between convergence speed and steady state mean square error of the NFSLMS algorithm, offers both fast convergence rate and low steady state error. Furthermore, by replacing the sigmoid function with the modified Versorial function, the modified CNFSLMS (MCNFSLMS) algorithm with low computational complexity is also presented. Experimental results illustrate that the combination scheme can behave as well as the best component and even better. Moreover, the MCNFSLMS algorithm requires less computational complexity than the CNFSLMS while keeping the same filtering performance.     

Analysis of the filtered-X LMS algorithm and a related new algorithm for active control of multitonal noise

In the presence of tonal noise generated by periodic noise source like rotating machines, the filtered-X LMS (FXLMS) algorithm is used for active control of such noises. However, the algorithm is derived under the assumption of slow adaptation limit and the exact analysis of the algorithm is restricted to the case of one real sinusoid in the literature. In this paper, for the general case of arbitrary number of sources, the characteristic polynomial of the equivalent linear system describing the FXLMS algorithm is derived and a method for calculating the stability limit is presented. Also, a related new algorithm free from the above assumption, which is nonlinear with respect to the tap weights, is proposed. Simulation results show that in the early stage of adaptation the new algorithm gives faster decay of errors.     

Ear Field Adaptive Noise Control for Snoring: An Real-time Experimental Approach

In this paper, an active noise control (ANC) system is developed to provide an effective and non-intrusive solution for reducing loud snoring to provide a quiet environment for a snorer's bed partner. An adaptive least mean square (LMS) algorithm optimized for different kinds of snore signals is introduced and theoretically analyzed. Also, a residual noise masking approach is proposed to further reduce the effect of the snore noise without interfering with the LMS algorithm. Computer simulations followed by real-time experiments are conducted to demonstrate the feasibility of the snore ANC systems based on a pillow setup. For the optimum effect based on the characteristics of human hearing, the performance of the proposed approach is evaluated by using the multi-channel feedforward ANC systems based on the filtered-X least mean square (FXLMS) algorithm. Compared with a traditional headboard setup for snoring noise control, the proposed snore ANC systems optimized for ear field operation yield much higher noise reduction around the ears of the snorer's bed partner.      

Pipelined robust M-estimate adaptive second-order Volterra filter against impulsive noise

The main limits on adaptive Volterra filters are their computational complexity in practical implementation and significant performance degradation under the impulsive noise environment. In this paper, a low-complexity pipelined robust M-estimate second-order Volterra (PRMSOV) filter is proposed to reduce the computational burdens of the Volterra filter and enhance the robustness against impulsive noise. The PRMSOV filter consists of a number of extended second-order Volterra (SOV) modules without feedback input cascaded in a chained form. To apply to the modular architecture, the modified normalized least mean M-estimate (NLMM) algorithms are derived to suppress the effect of impulsive noise on the nonlinear and linear combiner subsections, respectively. Since the SOV-NLMM modules in the PRMSOV can operate simultaneously in a pipelined parallelism fashion, they can give a significant improvement of computational efficiency and robustness against impulsive noise. The stability and convergence on nonlinear and linear combiner subsections are also analyzed with the contaminated Gaussian (CG) noise model. Simulations on nonlinear system identification and speech prediction show the proposed PRMSOV filter has better performance than the conventional SOV filter and joint process pipelined SOV (JPPSOV) filter under impulsive noise environment. The initial convergence, steady-state error, robustness and computational complexity are also better than the SOV and JPPSOV filters.     

二阶Volterra变记忆长度LMP算法

针对实际应用中非线性系统记忆长度未知致使Volterra自适应滤波器可能无法达到最优性能的问题,提出一种二阶Volterra变记忆长度LMP算法。利用Volterra滤波器二阶权系数矩阵的对称性和对称矩阵可对角化分解性质,推导得到了一阶权系数与二阶权系数个数相同的信号矢量与权系数矢量内积的二阶Volterra滤波器输出信号表达式;提出了基于DCT的二阶Volterra自适应滤波器（CSVF）及其LMP算法（CSVLMP）;采用FIR抽头长度的自适应调整思想,提出了基于DCT的二阶Volterra变记忆长度LMP算法（CSVMLMP）。记忆长度未知的非线性系统辨识的仿真结果表明,在[α]稳定分布噪声背景下,该算法在收敛速度、稳态性能和计算复杂度之间达到了较好的折中。     

Hierarchical partial update generalized functional link artificial neural network filter for nonlinear active noise control

To reduce the computational burden of the generalized FLANN (GFLANN) filter for nonlinear active noise control (NANC), a hierarchical partial update GFLANN (HPU-GFLANN) filter is presented in this paper. Based on the principle of divide and conquer, the proposed HPU-GFLANN divides the complex GFLANN filter (i.e., long memory length and large cross-terms selection parameter) into simple small-scale GFLANN modules and then interconnected in a pipelined form. Since those modules are simultaneously performed in a parallelism fashion, there is a significant improvement in computational efficiency. Besides, a hierarchical learning strategy is used to avoid the coupling effect between the nonlinear and linear part of the pipelined architecture. Data-dependent hierarchical M-Max filtered-error LMS algorithm is derived to selectively update coefficients of the HPU-GFLANN filter, which can further reduce the computational complexity. Moreover, the convergence analysis of the NANC system indicates that the proposed algorithm is stable. Computer simulation results verify that the proposed adaptive HPU-GFLANN filter is more effective in nonlinear ANC systems than the FLANN and GFLANN filters.     

基于总体平均经验模态分解的主动噪声控制系统研究

为了提高宽窄带混合噪声的消噪效果,本文提出一种基于总体平均经验模态分解（Ensemble empirical mode decomposition,EEMD）的主动噪声控制（Active noise control,ANC）系统,利用实时EEMD算法逐段将混合噪声分解成若干个固有模态函数（Intrinsic mode functions,IMF）分量.因为这些IMF分量的频带各不相同,所以实现了混合噪声中宽带分量和窄带分量的有效分离,独立进行ANC处理后成功解决了处理混合噪声时带来的“火花”现象,而且避免了传统混合ANC（Hybrid ANC,HANC）系统中频率失调的影响. EEMD算法也是对混合噪声的平稳化处理过程,因此当混合噪声中出现非平稳变化时,本文提出的系统也能保持较好的系统稳定性.通过不同噪声环境下进行仿真分析,提出的ANC系统比HANC系统具有更好的系统稳定性和更小的稳态误差.     

基于集员滤波的二阶Volterra自适应归一化最小平均P范数算法

针对Volterra非线性滤波算法计算复杂度呈幂级数增加的问题,提出了一种α稳定分布噪声下的基于集员滤波的二阶Volterra自适应滤波新算法。由于集员滤波的目标函数考虑了所有输入和期望输出的信号对,通过误差幅值的p次方的门限判决,更新Volterra滤波器的权向量,不仅有效降低了算法复杂度,而且提高了自适应算法对输入信号相关性的鲁棒性;并推导给出了权向量的更新公式。仿真结果表明,该算法计算复杂度低、收敛速度快,对噪声及输入信号相关性有较强的鲁棒性。     

基于2阶Renyi熵的自适应主动噪声控制

在前馈主动噪声控制中,基于均方误差准则的传统算法仅考虑了信号的2阶统计量,忽略了实际存在的非高斯信号,不能满足对非高斯噪声的控制要求.提出基于2阶Renyi熵的滤波X自适应有限脉冲响应 (finite impulse response,FIR)主动噪声控制算法,定义2阶Renyi熵作为性能指标,利用Parzen窗方法估计误差的概率密度函数,给出基于2阶Renyi熵的信息梯度下降算法,实现自适应FIR控制,同时分析了算法的收敛性和计算复杂度.对单频信号和实测宽带非高斯噪声的仿真结果表明该算法能很好地消除非高斯噪声.     

时域和酉空间中基于最大相关熵准则的非线性噪声处理

针对非线性噪声处理的问题,考虑到信号的高阶统计量以及在酉空间可以很好地处理非高斯噪声,提出了在时域和酉空间中基于最大相关熵准则（MCC）的噪声处理算法。结合MCC和梯度下降算法,设计出了时域中非线性噪声的滤波算法。同时将该算法推广到酉空间中噪声处理,给出了酉空间中基于MCC的滤波算法。通过仿真研究发现,在时域和酉空间中,基于MCC的滤波算法相对于传统的基于最小均方差（LMS）的滤波算法在处理非高斯噪声的问题时有着显著优势,以更快的收敛速度达到能够较完整地保留信号特征的效果。     

一种复合式宽带有源噪声控制算法

为获得更高的宽带噪声控制效果,提出了一种复合结构有源噪声控制算法.该算法将传统FXLMS算法和DFT-FSF算法并行运行,实现对宽带和窄带噪声的同时降噪.新的复合式算法对正弦波噪声实现高达45dB的降噪,而对带限白噪声的平均降噪量则达到15dB.仿真结果证明了算法的有效性.     

基于2级噪声检测的改进中值滤波算法

针对传统中值滤波算法去除高密度椒盐噪声能力的不足,提出了一种新的改进算法.该算法首先采用2级噪声检测方法对图像中的信号点和噪声点进行标识,然后对检测出的噪声点利用改进的中值滤波算法进行处理,而对信号点则保留其灰度值不变.实验结果表明,该算法能在有效去除噪声的同时很好地保留图像细节,相比于传统中值滤波及其它改进中值滤波算法,该算法获得的去噪后的图像具有更好的客观评价指标和主观视觉效果.     

Development of multivariable PID controller gains in presence of measurement noise

This paper examines the ability of a multivariable PID controller rejecting measurement noise without the use of any external filter. The work first provides a framework for the design of the PID gains comprising of necessary and sufficient conditions for boundedness of trajectories and zero-error convergence in presence of measurement noise. It turns out that such convergence requires time-varying gains. Subsequently, novel recursive algorithms providing optimal and sub-optimal time-varying PID gains are proposed for discrete-time varying linear multiple-input multiple-output (MIMO) systems. The development of the proposed optimal algorithm is based on minimising a stochastic performance index in presence of erroneous initial conditions, white measurement noise, and white process noise. The proposed algorithms are shown to reject measurement noise provided that the system is asymptotically stable and the product of the input–output coupling matrices is full-column rank. In addition, convergence results are presented for discretised continuous-time plants. Simulation results are included to illustrate the performance capabilities of the proposed algorithms.     

Output feedback control of a class of feedforward nonlinear systems in the presence of sensor noise

We propose an output feedback controller for a class of feedforward nonlinear systems under sensor noise. The sensor noise is any signal whose DC component is finite, which covers not only deterministic signals but also random signals including many practical noises. We introduce a notion of virtual state, then propose a measurement output feedback controller that utilizes a gain scaling factor. The gain scaling factor is commonly employed by the observer and controller. Through analysis, we show that all system states and output remain to be bounded in the presence of sensor noise, and the bound of states except output can be made arbitrarily small. Moreover, if the DC component of sensor noise is zero, the ultimate bound of the states and output can be made arbitrarily small by increasing the gain scaling factor in the presence of sensor noise. Copyright © 2013 John Wiley & Sons, Ltd.     

Active control of frequency varying disturbances in a diesel engine

In this paper a method for active vibration isolation of frequency varying tonal disturbances in an engine mounted on a raft is presented. An adaptive nonlinear control algorithm with frequency tracking is introduced to tackle this problem. The studied process is a true MIMO-system with strong cross-couplings and high background noise level. The controller performance is first validated by extensive simulations and then by test bed implementation. It is shown that the proposed method is robust to measurement noise and additional output disturbances, while yielding a high level of vibration suppression with fast convergence rate.     

Nonlinear channel equalization for QAM signal constellation usingartificial neural networks

Application of artificial neural networks (ANN's) to adaptive channel equalization in a digital communication system with 4-QAM signal constellation is reported in this paper. A novel computationally efficient single layer functional link ANN (FLANN) is proposed for this purpose. This network has a simple structure in which the nonlinearity is introduced by functional expansion of the input pattern by trigonometric polynomials. Because of input pattern enhancement, the FLANN is capable of forming arbitrarily nonlinear decision boundaries and can perform complex pattern classification tasks. Considering channel equalization as a nonlinear classification problem, the FLANN has been utilized for nonlinear channel equalization. The performance of the FLANN is compared with two other ANN structures [a multilayer perceptron (MLP) and a polynomial perceptron network (PPN)] along with a conventional linear LMS-based equalizer for different linear and nonlinear channel models. The effect of eigenvalue ratio (EVR) of input correlation matrix on the equalizer performance has been studied. The comparison of computational complexity involved for the three ANN structures is also provided.     

Computationally efficient active noise reduction in headsets

In order to improve the passive attenuation of hearing protection headsets, active noise reduction (ANR) techniques are usually applied. These ANR-techniques accomplish the active attenuation of the disturbing acoustical noise using an out-of-phase antinoise. The antinoise destructively interferes with the disturbing noise close to the humans ear drum. The generation of the antinoise can be conducted using different control strategies. However, due to variable system plants, often adaptive control strategies are chosen. Even though such adaptive systems effectively attenuate the disturbing noise in a wide frequency range, a major disadvantage is the computational effort linked to the large amount of controller parameters. The controller parameters have tobe updated by the adaptive algorithm and the resulting computational effort makes the application of expensive digital signal processors unavoidable. For this reason, no commercial products realizing adaptive broadband techniques are on the market yet. In this paper, a partially-adaptive control approach is introduced which permits the reduction of the computational effort in comparison to conventional and fully adaptive ANR-controllers. The noise reduction performance as well as the computational efficiency of the proposed control strategy is presented.     

A stochastic PID controller for a class of MIMO systems

This paper deals with the design of a controller possessing tracking capability of any realisable reference trajectory while rejecting measurement noise. We consider discrete-time-varying multi-input multi-output stable linear systems and a proportional-integral-derivative (PID) controller. A novel recursive algorithm estimating the time-varying PID gains is proposed. The development of the proposed algorithm is based on minimising a stochastic performance index. The implementation of the proposed algorithm is described and boundedness of trajectories and convergence characteristics are presented for a discretised continuous-time model. Simulation results are included to illustrate the performance capabilities of the proposed algorithm.