Identification and control for heart rate regulation during treadmill exercise

This paper proposes a novel integrated approach for the identification and control of Hammerstein systems to achieve desired heart rate profile tracking performance for an automated treadmill system. For the identification of Hammerstein systems, the pseudorandom binary sequence input is employed to decouple the identification of dynamic linear part from input nonlinearity. The powerful epsilon-insensitivity support vector regression method is adopted to obtain sparse representations of the inverse of static nonlinearity in order to obtain an approximate linear model of the Hammerstein system. An Hinfinity controller is designed for the approximated linear model to achieve robust tracking performance. This new approach is successfully applied to the design of a computer-controlled treadmill system for the regulation of heart rate during treadmill exercise. Minimizing deviations of heart rate from a preset profile is achieved by controlling the speed of the treadmill. Both conventional proportional-integral-derivative (PID) control and the proposed approaches have been employed for the controller design. The proposed algorithm achieves much better heart rate tracking performance.     

A stable adaptive Hammerstein filter employing partial orthogonalization of the input signals

This paper presents an algorithm that adapts the parameters of a Hammerstein system model. Hammerstein systems are nonlinear systems that contain a static nonlinearity cascaded with a linear system. In this paper, the static nonlinearity is modeled using a polynomial system, and the linear filter that follows the nonlinearity is an infinite-impulse response (IIR) system. The adaptation of the nonlinear components is improved by orthogonalizing the inputs to the coefficients of the polynomial system. The step sizes associated with the recursive components are constrained in such a way as to guarantee bounded-input bounded-output (BIBO) stability of the overall system. This paper also presents experimental results that show that the algorithm performs well in a variety of operating environments, exhibiting stability and global convergence of the algorithm.     

Identification of certain time-varying nonlinear Wiener andHammerstein systems

The problem of identification and tracking of time-varying nonlinear systems is addressed. In particular, the Wiener system that consists of a dynamic time-varying linear part followed by a fixed nonlinearity and the Hammerstein system in which the order of these two blocks is reversed are studied. The extended Kalman filter (EKF) algorithm is applied. It is also shown that this algorithm can be reformulated in terms of a nonlinear minimization problem with a quadratic inequality constraint in order to ensure exponential stability, resulting in the algorithm CEKF. As indicated by means of numerical examples, this latter algorithm is less sensitive to the chosen initialization than the EKF. The proposed algorithms depend on certain second-order statistics that may be unknown in a typical scenario. A method for estimation of these quantities is proposed. It is demonstrated that the suggested algorithms can be successfully applied to the problem of acoustic echo cancelation     

Identification of Hammerstein models with cubic spline nonlinearities

This paper considers the use of cubic splines, instead of polynomials, to represent the static nonlinearities in block structured models. It introduces a system identification algorithm for the Hammerstein structure, a static nonlinearity followed by a linear filter, where cubic splines represent the static nonlinearity and the linear dynamics are modeled using a finite impulse response filter. The algorithm uses a separable least squares Levenberg-Marquardt optimization to identify Hammerstein cascades whose nonlinearities are modeled by either cubic splines or polynomials. These algorithms are compared in simulation, where the effects of variations in the input spectrum and distribution, and those of the measurement noise are examined. The two algorithms are used to fit Hammerstein models to stretch reflex electromyogram (EMG) data recorded from a spinal cord injured patient. The model with the cubic spline nonlinearity provides more accurate predictions of the reflex EMG than the polynomial based model, even in novel data.     

Quantifying and cancelation memory effects in high power amplifier for OFDM systems

A modeling approach to power amplifier design for implementation in OFDM radio units is presented. The power amplifier model assesses the impact of linear memory effects within the system using a Wiener representation, and employs a linear novel parametric estimation technique using Hilbert space. In addition, in order to model the nonlinear memory effects the previous topology is generalized by inserting the truncated Volterra filter before the static nonlinearity. Predistortion based on the Hammerstein model is introduced to deal with the nonlinear response. The new general algorithm is proposed to evaluate the Hammerstein model parameters for an OFDM system. A representative test bed was designed and implemented. The assessment of the new methods for PA and PD modeling are confirmed by experimental measurements. The measurement results reveal the preference of the new techniques over the existing approaches.     

A Nonlinear Acoustic Echo Canceller Using Sigmoid Transform in Conjunction With RLS Algorithm

Nonlinearity of amplifiers and/or loudspeakers gives rise to nonlinear echo in acoustic systems, which seriously degrades the performance of speech and audio communications. Many nonlinear acoustic echo cancellation (AEC) methods have been proposed. In this paper, a simple yet efficient nonlinear echo cancellation scheme is presented by using an adaptable sigmoid function in conjunction with a conventional transversal adaptive filter. The new scheme uses the least mean square (LMS) algorithm to update the parameters of sigmoid function and the recursive least square (RLS) algorithm to determine the coefficient vector of the transversal filter. The proposed AEC is proved to be convergent under some mild assumptions. Computer simulations show that the proposed scheme gives a superior echo cancellation performance over the well known Volterra filter approach when the echo path suffers from the saturation-type nonlinear distortion. More importantly, the new AEC has a much lower computational complexity than the Volterra-filter-based method.      

Networked Predictive Control Systems Based on the Hammerstein Model

In this paper, a novel predictive control-based approach is proposed for a networked control system with random delays containing an input nonlinear process based on a Hammerstein model. The method uses a time-delay two-step generalized predictive control scheme, which consists of two parts: one is to deal with the input nonlinearity of the Hammerstein model and the other is to compensate for the network-induced delay in the networked control system. A theoretical result using the Popov criterion is presented for the closed-loop stability of the system in the case of a constant delay. Simulation examples illustrating the validity of the approach are also presented.     

Decomposition Based Newton Iterative Identification Method for a Hammerstein Nonlinear FIR System with ARMA Noise

This paper derives a Newton iterative algorithm for identifying a Hammerstein nonlinear FIR system with ARMA noise (i.e., Hammerstein nonlinear controlled autoregressive moving average system). This method decomposes a Hammerstein nonlinear system into two subsystems using the hierarchical identification principle, estimating the parameters of the system directly without using the over-parameterization method. The simulation results show that the proposed algorithm is effective.     

变遗忘因子递推最小二乘Hammerstein系统辨识算法

摘.要:本论文研究了单输入单输出非线性Hammerstein系统的辨识问题,提出了一种具有变遗忘因子的递推最小二乘算法.由于Hammerstein系统模型的非线性特征,传统的递推最小二乘算法无法直接用来解决该系统的辨识问题.为此,论文将Hammerstein系统参数进行了映射变换,使得变换后的系统参数与Hammerst...     

基于非线性预处理的声回波对消

考虑到扬声器的非线性特性,将实际房间声学系统模拟为一个无记忆非线性系统与一个动态线性系统相级联,并分别采用多项式函数和限幅函数模拟扬声器系统的非线性特性,通过在声回波对消的归一化最小均方算法中引入相应的非线性预处理,改善房间声回波对消的效果.计算机仿真证明,考虑了扬声器非线性影响并在算法中进行相应非线性预处理后的房间声回波对消比未经非线性预处理的声回波对消有更好的对消效果.     

非线性信道的均衡算法研究

本文使用Hammerstein模型和维纳模型代替Volterra级数模型来模拟非线性结构以降低运算复杂度,提出了一个由Hammerstein模型和维纳模型构建成的非线性信道传输系统的模型.基于该系统模型,分别提出并推导了三种非线性信道的均衡算法:NCRLS算法、NCKalman算法和NCRPEM算法,并对这三种新算法的性能进行了比较.仿真结果表明,在剩余均方误差方面三种算法中NCKalman算法最小,NCRPEM算法次之,NCRLS算法较差;在收敛速度方面NCRPEM算法收敛最快,NCRLS算法次之,NCKalman算法较差.     

On the LMS algorithm with constant and variable leakage factor in a nonlinear environment

This paper studies the application of the linear least-mean-square (LMS) algorithm with leakage to a system having a memoryless saturation-type nonlinearity. This approach represents an interesting alternative to the nonlinear LMS (NLLMS) algorithm. The major drawback to implement the latter is that the model parameters of the system nonlinearity must be known. In contrast, the linear LMS algorithm with leakage does not require such knowledge. It permits to approximate the performance of the NLLMS by properly selecting a constant leakage factor value. To cope with an eventual change of the environment, a strategy for a variable leakage is also proposed. Several numerical simulations are presented with the aim of ratifying the feasibility of the proposed approach.     

Nonparametric identification of Hammerstein systems

A discrete-time nonlinear Hammerstein system is identified, and the correlation and frequency-domain methods for identification of its linear subsystem are presented. The main results concern the estimation of the nonlinear memoryless subsystem. No conditions concerning the functional form of the transform characteristic of the subsystem are made, and an algorithm for estimation of the characteristic is given. The algorithm is simply a nonparametric kernel estimate of the regression function calculated from dependent data. It is shown that the algorithm converges to the characteristic of the subsystem regardless of the probability distribution of the input variable. Pointwise as well as global consistencies are established. For Lipschitz characteristics the rate of the convergence in probability is O(n^-1/3 )     

Disturbance-Observer-Based Hysteresis Compensation for Piezoelectric Actuators

We present a novel hysteresis compensation method for piezoelectric actuators. We consider the hysteresis nonlinearity of the actuator as a disturbance over a linear system. A disturbance observer (DOB) is then utilized to estimate and compensate for the hysteresis nonlinearity. In contrast to the existing inverse-model-based approach, the DOB-based hysteresis compensation does not rely on any particular hysteresis model, and therefore provides a simple and effective compensation mechanism. We design and fabricate a lead magnesium niobate-lead titanate (PMN-PT) piezoelectric actuator for microscale tip-based power sintering process. Experimental validation of the proposed hysteresis compensation is performed on the PMN-PT cantilever piezoelectric actuator. The experimental results demonstrate the effectiveness and efficiency of the approach.      

Post-Nonlinear Blind Extraction in the Presence of Ill-Conditioned Mixing

An extension of blind source extraction (BSE) of one or a group of sources to the case of ill—conditioned and post-nonlinear (PNL) mixing is introduced. This is achieved by a “mixed objective” type of cost function which jointly maximizes the kurtosis of a recovered source and estimates a measure of nonlinearity within the mixing system. This helps to circumvent problems with existing BSE methods, which are limited to noiseless and linear mixing models. Simulations illustrate the performance of the proposed algorithm and its usefulness, especially in the presence of very ill-conditioned mixing systems.      

一种变体BISON分组密码算法及分析

该文基于Whitened Swap?or?Not(WSN)的结构特点,分析了Canteaut 等人提出的Bent whItened Swap Or Not –like (BISON-like) 算法的最大期望差分概率值(MEDP)及其(使用平衡函数时)抵御线性密码分析的能力;针对BISON算法迭代轮数异常高(一般为3n轮,n为数据分组长度)且密钥信息的异或操作由不平衡Bent函数决定的情况,该文采用了一类较小绝对值指标、高非线性度、较高代数次数的平衡布尔函数替换BISON算法中的Bent函数,评估了新变体BISON算法抵御差分密码分析和线性密码分析的能力。研究结果表明:新的变体BISON算法仅需迭代n轮;当n较大时(如n=128或256),其抵御差分攻击和线性攻击的能力均接近理想值。且其密钥信息的异或操作由平衡函数来决定,故具有更好的算法局部平衡性。     

Wiener功率放大器预失真中LMS Newton算法的研究

基于Wiener模型构建的功率放大器和Hammerstein模型构建的预失真器,在现有的LMS算法基础上,结合预失真系统模型,推导出NFLMS(Nonlinear Filtered LMS)预失真算法.在此基础上,提出NFLMS Newton预失真算法的概念,为了达到实用目的,本文提出并推导了一种改进型NFLMS Newton预失真算法.仿真结果表明,改进型NFLMS Newton预失真算法和NFLMS预失真算法相比明显加快了收敛速度,并且快速降低了算法的剩余误差.     

基于l2,1范数和神经网络的非线性特征选择方法

在基于l_2,1范数的特征选择方法中,l_2,1范数可以使选择的特征具有组间稀疏性和组内稀疏性,同时还可以去除特征数据中的异常值。然而,大多数基于l2,1范数的特征选择算法常通过线性方程求解,无法探究特征之间的非线性关系。因此,本文提出了一种基于l2,1范数的非线性特征选择方法,将l_2,1范数与神经网络相结合。一方面,该方法利用神经网络的非线性特性对l_2,1范数进行求解。另一方面,该方法利用l2,1范数实现基于神经网络框架的特征选择。最后,本文将该方法与当前流行的特征选择方法在八个公开数据集进行了对比,实验结果验证了该方法具有一定的优越性。      

非线性信道的LMS Newton均衡算法的研究

本文使用Hammerstein模型和维纳模型代替Volterra级数模型来模拟非线性结构以降低运算复杂度,提出了一个由Hammerstein模型和维纳模型构建成的非线性信道传输系统的模型,由此模型给出并推导出了基于该信道模型的NCLMS算法、改进1型NCLMS Newton算法和改进2型NCLMS Newton算法.仿...     

Fast LMS/Newton Algorithms for Stereophonic Acoustic Echo Cancelation

This paper presents a new class of adaptive filtering algorithms to solve the stereophonic acoustic echo cancelation (AEC) problem in teleconferencing systems. While stereophonic AEC may be seen as a simple generalization of the well-known single-channel AEC, it is a fundamentally far more complex and challenging problem to solve. The main reason being the strong cross correlation that exists between the two input audio channels. In the past, nonlinearities have been introduced to reduce this correlation. However, nonlinearities bring with it additional harmonics that are undesirable. We propose an elegant linear technique to decorrelate the two-channel input signals and thus avoid the undesirable nonlinear distortions. We derive two low complexity adaptive algorithms based on the two-channel gradient lattice algorithm. The models assume the input sequences to the adaptive filters to be autoregressive (AR) processes whose orders are much lower than the lengths of the adaptive filters. This results in an algorithm, whose complexity is only slightly higher than the normalized least-mean-square (NLMS) algorithm; the simplest adaptive filtering method. Simulation results show that the proposed algorithms perform favorably when compared with the state-of-the-art algorithms.