Linear approximation model network and its formation via evolutionary computation

To overcome the deficiency of ’local model network’ (LMN) techniques, an alternative ’linear approximation model’ (LAM) network approach is proposed. Such a network models a nonlinear or practical system with multiple linear models fitted along operating trajectories, where individual models are simply networked through output or parameter interpolation. The linear models are valid for the entire operating trajectory and hence overcome the local validity of LMN models, which impose the predetermination of a scheduling variable that predicts characteristic changes of the nonlinear system. LAMs can be evolved from sampled step response data directly, eliminating the need for local linearisation upon a pre-model using derivatives of the nonlinear system. The structural difference between a LAM network and an LMN is that the overall model of the latter is a parameter-varying system and hence nonlinear, while the former remains linear time-invariant (LTI). Hence, existing LTI and transfer function theory applies to a LAM network, which is therefore easy to use for control system design. Validation results show that the proposed method offers a simple, transparent and accurate multivariable modelling technique for nonlinear systems.     

基于遗传算法的非线性迟滞系统参数识别

研究了非线性迟滞系统的参数识别问题。在识别过程中,将非线性迟滞系统的记忆复力用双折线模型来描述,并由此模型写出非线性迟滞系统的参数识别方程。利用相干函数,把关于模型参数非线性的参数识别方程转化线性参数识别前提下的非线性函数优化问题。     

非线性结构的参数估计和系统识别

非线性结构的参数估计和系统识别是取得和验证非线性结构数学模型的一门学科.本文综述了国内外非线性结构参数估计和系统识别的发展状况,较系统地介绍了非线性结构的数学模型、单自由度、多自由度系统的参数估计方法和非参数模型的识别方法.     

Efficient algorithms for ordinary differential equation model identification of biological systems

Algorithms for parameter estimation and model selection that identify both the structure and the parameters of an ordinary differential equation model from experimental data are presented. The work presented here focuses on the case of an unknown structure and some time course information available for every variable to be analysed, and this is exploited to make the algorithms as efficient as possible. The algorithms are designed to handle problems of realistic size, where reactions can be nonlinear in the parameters and where data can be sparse and noisy. To achieve computational efficiency, parameters are mostly estimated for one equation at a time, giving a fast and accurate parameter estimation algorithm compared with other algorithms in the literature. The model selection is done with an efficient heuristic search algorithm, where the structure is built incrementally. Two test systems are used that have previously been used to evaluate identification algorithms, a metabolic pathway and a genetic network. Both test systems were successfully identified by using a reasonable amount of simulated data. Besides, measurement noise of realistic levels can be handled. In comparison to other methods that were used for these test systems, the main strengths of the presented algorithms are that a fully specified model, and not only a structure, is identified, and that they are considerably faster compared with other identification algorithms.     

Nonlinear bearing stiffness parameter estimation in flexible rotor–bearing systems using Volterra and Wiener approach

Higher order frequency response functions based on Volterra and Wiener series are explored for the inverse problem of stiffness estimation of a flexible rotor supported in nonlinear bearings. The Volterra series has been employed by researchers earlier for the identification of higher order kernels of nonlinear systems through a non-parametric approach. The present study investigates the possibility of employing these kernels for parameter estimation of the system. Numerical simulation has been carried out for a system with three degrees of freedom and with cubic nonlinearity in stiffness. A frequency domain has been adopted for the identification of higher order kernels. The procedure involves extraction of Wiener kernels from the response of the system to a Gaussian white noise excitation. Volterra kernels are in turn synthesised from the Wiener kernels. In addition to direct kernels, the system under consideration, requires definitions of cross-kernels and their estimation. Expressions for the cross and direct kernels are constructed in the frequency domain. A set of third-order kernel factors are algebraically and graphically synthesised from the measured first-order kernels. These third-order kernel factors are then processed with the measured third-order kernels for nonlinear parameter estimation. Damping is taken to be linear in the analysis. The procedure is illustrated through numerical simulation. The assumptions involved and the approximations are discussed.     

Flower Pollination Heuristics for Nonlinear Active Noise Control Systems

Abstract In this paper, a novel design of the flower pollination algorithm is presented for model identification problems in nonlinear active noise control systems. The recently introduced flower pollination based heuristics is implemented to minimize the mean squared error based merit/cost function representing the scenarios of active noise control system with linear/nonlinear and primary/secondary paths based on the sinusoidal signal, random and complex random signals as noise interferences. The flower pollination heuristics based active noise controllers are formulated through exploitation of nonlinear filtering with Volterra series. The comparative study on statistical observations in terms of accuracy, convergence and complexity measures demonstrates that the proposed meta-heuristic of flower pollination algorithm is reliable, accurate, stable as well as robust for active noise control system. The accuracy of the proposed nature inspired computing of flower pollination is in good agreement with the state of the art counterpart solvers based on variants of genetic algorithms, particle swarm optimization, backtracking search optimization algorithm, fireworks optimization algorithm along with their memetic combination with local search methodologies. Moreover, the central tendency and variation based statistical indices further validate the consistency and reliability of the proposed scheme mimic the mathematical model for the process of flower pollination systems.     

基于全局非线性可分离的最小二乘法的飞机颤振模态参数辨识

本文考虑在输入-输出数据都带有噪声的前提下,将偏差补偿最小二乘算法（CLS）进行推广得到非线性可分离的最小二乘算法（NSLS）。采用适用于噪声环境的非线性可分离的最小二乘算法可准确地辨识飞机的颤振模态参数,该算法结合传递函数模型,将带噪声系统的辨识问题转化为非线性可分离的最小二乘问题。利用该算法,两噪声的方差值和传递函数中的模型参数可分离地估计出来。最后利用试飞试验数据辨识飞机的系统参数,验证了该方法的有效性。     

Precise identification of moving vehicular parameters based on improved glowworm swarm optimization algorithm

This paper proposes an indirect method for the identification of moving vehicular parameters using the dynamic responses of the vehicle. The moving vehicle is modelled as 2-DOF system with 5 parameters and 4-DOF system with 12 parameters, respectively. Finite element method is used to establish the equation of the coupled bridge–vehicle system. The dynamic responses of the system are calculated by Newmark direct integration method. The parameter identification problem is transformed into an optimization problem by minimizing errors between the calculated dynamic responses of the moving vehicle and those of the simulated measured responses. Glowworm swarm optimization algorithm (GSO) is used to solve the objective function of the optimization problem. A local search method is introduced into the movement phase of GSO to enhance the accuracy and convergence rate of the algorithm. Several test cases are carried out to verify the efficiency of the proposed method and the results show that the vehicular parameters can be identified precisely with the present method and it is not sensitive to artificial measurement noise.     

Synchronization in a steer-by-wire vehicle dynamic system

This work verifies the chaotic motion of a steer-by-wire vehicle dynamic system, and then elucidates an application of the synchronization to a vehicle model to control the chaos. The largest Lyapunov exponent is estimated from the synchronization to identify periodic and chaotic motions. Then, a bifurcation diagram reveals complex nonlinear behaviors over a range of parameter values. Finally, a continuous feedback control method based on the synchronization characteristics is presented to control a chaotic vehicle handling and steering system. The designed controller is demonstrated to work quite well for nonlinear systems in achieving robust stability and protecting the vehicle from slip or spin. Some simulation results are presented to establish the feasibility of the proposed method.     

基于改进无迹卡尔曼滤波的锂电池SOC在线估计

针对无迹卡尔曼滤波算法对电池模型敏感并且容易受到不确定噪声干扰的问题,提出了改进的无迹卡尔曼滤波算法（improved unscented Kalman filter,IUKF）,提高电池荷电状态（State of charge,SOC）估计精度和鲁棒性能。首先,对锂离子电池进行建模并完成参数离线辨识。紧接着,对模型参数进行敏感性分析,研究不同参数对SOC估计效果的影响程度,为模型参数自适应对象的选取提供依据。随后,研究了包含模型自适应算法和噪声自适应算法在内的IUKF算法实现过程。最后,通过物理实验对比分析了IUKF与其它算法的实际估计效果,实验结果表明,该方法估计误差小于1.79%,鲁棒性能良好。     

Sensor fault diagnosis for nonlinear processes with parametric uncertainties

This paper addresses the problem of detecting, discriminating, and reconstructing sensor faults for nonlinear systems with known model structure but uncertainty in the parameters of the process. The convenience of the proposed technique lies in the fact that historical operational data and/or a priori fault information is not required to achieve accurate fault reconstruction except for fixed, short intervals. The overall fault diagnosis algorithm is composed of a series of nonlinear estimators, which estimates parameter and a fault isolation and identification filter. Parameter estimation and fault reconstruction cannot be performed accurately since faults and parametric uncertainty interact with each other. Therefore, these two tasks are performed at different time scales, where the fault diagnosis takes place at a more frequent rate than the parameter estimation. It is shown that the fault can be reconstructed under some realistic assumptions and the performance of the proposed methodology is evaluated on a simulated chemical process exhibiting nonlinear dynamic behavior.     

基于Wiener-Hammerstein模型的一种系统辨识方法

针对工业大系统中Wiener-Hammerstein模型,提出一种新的辨识方法。该方法结合分散辨识对线性系统辨识精度高的优点与混合粒子群优化解决非线性、不可微和多峰值的复杂问题的长处,进行复合控制,并利用计量学中的动态计量方法,建立动态计量仿真模型。仿真研究与实验结果表明,该方法应用在非线性分布参数系统辨识中可有效提高辨识精度。     

Joint estimation and identification of lidar log power returns in a switching environment

The problem of estimating the return power in a laser integrated radar (lidar) system in the presence of multiplicative noise and partially unmodeled dynamics is explored. Several nonlinear methodologies are reviewed and compared to develop a systematic approach to signal model identification and estimation. The situations considered operate in mode-switching environments, that is, the desired unknown parameters are allowed to vary according to sudden jumps exhibiting discontinuous behavior at random times. Partitioning-based, parallel-structured techniques are shown to be significantly superior to the usual extended Kalman filter algorithm.     

非线性参数结构系统的参数识别

本对基于时域信息的非线性参数系统参数识别问题进行了研究。首先提出了一种非线性参数系统的分类方法。然后对非线性参数系统的参数化问题进行了讨论：本提出用NEWTON RAPHSON方法进行非线性参数系统识别方程的求解，并用埃特金加速法提高迭代收敛的速度。章的最后以剪力墙参数的识别问题为例演示了本所提方法的有效性。数值算例表明，识别结果的精度和识别耗时对参数初值的选取并不敏感。     

线性回归模型中的递推辨识及其应用

摘要：因机械系统振动响应离散差分方程可以改写为关于结构模态参数的线性回归形式,对于此线性回归形式中的结构模态参数的辨识问题可转化为系统辨识理论中线性回归模型的未知参数矢量的辨识估计问题。当系统对象在白噪声激励下,常规的最小二乘辨识法可以给出参数估计的无偏估计。当系统对象在有色噪声作用下,在常规最小二乘辨识法的基础上提出一种新的可分离迭代最小二乘辨识法。在缺乏关于噪声的统计先验信息,仅有噪声为未知但有界的情况下,提出一种带死区的最小二乘辨识法,该辨识法不仅能给出未知参数矢量的一致性无偏估计,而且还能保证参数估计值逐渐向真值靠拢,任何相邻两估计值之间的逼近程度远远小于噪声的上界。在有界噪声出现的情况下,该算法的鲁棒性可以通过在参数修正方程中引入死区的方法来增强。最后用理论上的仿真算例和弹簧-质量-阻尼系统的振动响应来验证两方法的有效性和可行性。     

Concatenated fibre-wireless channel identification in a multiuser CDMA environment

Radio-over-fibre (ROF) has received increasing attention for its ability to enable broadband wireless access. This fibre-based wireless access scheme meets the demand for broadband service by integrating the high capacity of optical networks with the flexibility of radio networks (the optical and wireless channels are concatenated with one another). There are, however, impairments that come with this appealing technology. The nonlinear distortion of the optical link and the multipath dispersion of the wireless channel are two of the major factors. In order to limit the effects of these distortions, estimation, and subsequently equalisation, of the concatenated fibre- wireless channel needs to be done. An estimation algorithm for the fibre-wireless uplink in a multiuser code division multiple access (CDMA) environment is presented using pseudonoise training sequences. It has already been shown by Fernando et al. (2001) that identification of the fibre- wireless uplink is possible in a single user CDMA environment. However, the more difficult task of identification in a multiuser spread spectrum environment, which is more realistic, is shown. In the multiuser case, the cumulative effect of multiuser interference, multipath dispersion, nonlinear distortion and noise should all be handled together which makes it more challenging. Numerical evaluations of the developed algorithm show that a good estimation of both the linear and nonlinear systems is possible in the presence of 16 independent users and an signal- to-noise ratio (SNR) of 22 dB. The estimation accuracy increases with the length of the PN sequence.     

Deterministic inference for stochastic systems using multiple shooting and a linear noise approximation for the transition probabilities

Estimating model parameters from experimental data is a crucial technique for working with computational models in systems biology. Since stochastic models are increasingly important, parameter estimation methods for stochastic modelling are also of increasing interest. This study presents an extension to the ‘multiple shooting for stochastic systems (MSS)’ method for parameter estimation. The transition probabilities of the likelihood function are approximated with normal distributions. Means and variances are calculated with a linear noise approximation on the interval between succeeding measurements. The fact that the system is only approximated on intervals which are short in comparison with the total observation horizon allows to deal with effects of the intrinsic stochasticity. The study presents scenarios in which the extension is essential for successfully estimating the parameters and scenarios in which the extension is of modest benefit. Furthermore, it compares the estimation results with reversible jump techniques showing that the approximation does not lead to a loss of accuracy. Since the method is not based on stochastic simulations or approximative sampling of distributions, its computational speed is comparable with conventional least‐squares parameter estimation methods.Inspec keywords: stochastic systems, parameter estimation, probability, least squares approximationsOther keywords: deterministic inference, stochastic systems, multiple shooting, linear noise approximation, transition probabilities, systems biology, parameter estimation methods, likelihood function, normal distributions, intrinsic stochasticity effects, reversible jump techniques, approximative sampling, conventional least‐squares parameter estimation methods     

非线性结构系统识别的自组织蒙特卡洛滤波方法

摘 要：提出一种对扩展的非线性状态空间模型进行识别的自组织序贯蒙特卡洛滤波方法. 通过在原始状态空间模型中增加未知待识别参数来定义扩展的状态空间模型,该模型为自组织非线性状态空间模型,适用于解决噪声分布的自校正问题;同时该系统识别方法通过引入局部似然函数,可以从有限的有效数据中进行最优参数选取.给出了Bouc-Wen滞回系统识别的数值算例,证明了该方法的有效性.     

一种非线性振动系统参数辨识模型

本文探讨了一种适用于非线性振动系统参数辨识的模型,并对该模型的建模方法,模型参数估计,模型的相关检验等作了详细叙述,并对模型进行了仿真计算。仿真考核计算表明,该模型具有结构简洁,对输入无限制,计算工作量小等优点。     

既有空间结构位形推算的随机偏差方法

为了有效地解决时域非线性损伤的识别问题,提出了基于线性/非线性自回归一般表达式模型(general expression for linear and nonlinear autoregressive model,GNAR模型)和Itakura距离的损伤识别方法。描述了GNAR模型的基本理论,给出了基本的模型定阶和参数估计方法。采用剪枝算法对GNAR模型结构进行了优化选取,并提出了以Itakura距离作为损伤指标的非线性损伤识别方法。采用3层框架非线性损伤实验验证了该方法的有效性,并将提出的方法运用于输电塔钢架模型的非线性损伤识别实验中。结果表明：提出的结构非线性损伤识别方法对框架和输电塔钢架结构的非线性损伤识别效果良好,且环境变化对其识别结果影响较小,由该方法计算得到的损伤层损伤概率明显大于未损伤层,有利于高效地确定非线性损伤源的位置。