Parameter estimation of an asymmetric vocal-fold system from glottal area time series using chaos synchronization

In this paper, we apply an iterative parameter adaption scheme based on chaos synchronization to estimate system parameters of the asymmetric vocal folds from glottal area time series. The original asymmetric vocal-fold system associated with recurrent laryngeal paralysis shows chaotic vibrations with positive Lyapunov exponents. Aperiodic glottal area time series from the original system will be applied as the feedback variable coupling the simulative and the original vocal-fold systems. The parameter adaption technique based on chaos synchronization is employed to manipulate the simulative system parameters. The chaotic vibrations, system parameters, and the bifurcation diagram of the original vocal-fold system can be exactly reproduced in the simulative system, and the two chaotic systems can be synchronized. Furthermore, the effects of noise, sampling rate, and equation difference due to nonlinear spring terms on vocal-fold parameter estimations are investigated. Despite large noise perturbations, large equation differences, and low sampling rate, the parameter adaption scheme can effectively estimate the original vocal-fold system parameters. This study provides a theoretical base to apply chaos synchronization to estimate the vocal-fold system parameters from the glottal area data and show its potential application in laryngeal physiology.     

Chen氏混沌电路实现与同步控制实验研究

对Chen氏混沌系统的电路实现作了进一步的研究，推导出切实可行的电路参数.在此基础上利用Electronic Workbench电路仿真软件进行了仿真实验，并搭建实现了实际的Chen氏电路，在示波器上观察到了Chen氏混沌系统的混沌波形.同时，利用单变量耦合反馈控制方法对两个Chen氏系统的同步控制进行了实验研究，给出了实现同步的电路参数调节范围. 关键词： 混沌系统 Chen氏混沌 同步控制 电路实现     

Adaptive synchronization of memristor-based Chuaʼs circuits

In this Letter, a memristor-based Chua?s system is presented, and the chaotic behavior of this system is demonstrated by phase portraits. This Letter also deals with the problem of adaptive synchronization control of this chaotic system using the drive–response concept, and presents an adaptive control scheme for the synchronization of memristor-based Chua?s circuit, when the parameters of the drive system are fully unknown and different with those of the response system. The sufficient condition for the adaptive synchronization has been analyzed. Moreover, the controller design method is further extended to more general cases, where the physical plant contains parameter uncertainties, represented in either polytopic or structured frameworks. Numerical simulations are used to demonstrate these results.     

基于金属氧化物半导体晶体管Colpitts混沌振荡电路及其同步研究

用金属氧化物半导体(MOS)晶体管模型取代传统Colpitts混沌振荡电路中的三极管模型, 提出了一种基于MOS管的Colpitts混沌振荡电路. 通过合适的归一化方法, 得到了与基于三极管电路类似的状态模型. 平衡点的指标说明两种结构产生混沌的机理并不相同. 然后, 通过参数反演, 得到了详细的电路参数, 并用Pspice软件仿真得到了混沌吸引子和混沌信号的频谱图, 说明了此结构可在低电压下工作并且能产生高频率的混沌信号. 最后, 用误差反馈的方法实现了这种结构的同步. 关键词： Colpitts混沌 金属氧化物半导体晶体管 低电压 误差反馈同步     

On a recursive method for the estimation of unknown parameters of partially observed chaotic systems

We investigate a recently proposed method for on-line parameter estimation and synchronization in chaotic systems. This novel technique has been shown effective to estimate a single unknown parameter of a primary chaotic system with known functional form that is only partially observed through a scalar time series. It works by periodically updating the parameter of interest in a secondary system, with the same functional form as the primary one but no explicit coupling between their dynamic variables, in order to minimize a suitably defined cost function. In this paper, we review the basics of the method, and investigate its robustness and new extensions. In particular, we study the performance of the novel technique in the presence of noise (either observational, i.e., an additive contamination of the observed time series, or dynamical, i.e., a random perturbation of the system dynamics) and when there is a mismatch between the primary and secondary systems. Numerical results, including comparisons with other techniques, are presented. Finally, we investigate the extension of the original method to perform the estimation of two unknown parameters and illustrate its effectiveness by means of computer simulations.     

一个改进恒Lyapunov指数谱混沌系统的电路实现与同步控制

基于提出的恒Lyapunov指数谱混沌系统,通过将系统中的参数进行剥离,得到一个改进型的恒Lyapunov指数谱混沌系统.该混沌系统存在三个重要的特性：双参数恒Lyapunov指数谱、存在全局线性调幅参数和倒相参数.通过Lyapunov指数谱与分岔图结合理论证明与推理,揭示了该新系统存在的上述动力学特征.构建实验电路,实现了改进混沌系统,物理实验验证了新系统的混沌行为.最后,利用单变量反馈控制方法实现了新系统的同步控制,通过物理实验验证了新系统同步控制的条件. 关键词： 改进恒Lyapunov指数谱混沌系统 电路实现 同步控制     

Identifying parameter by identical synchronization between different systems

In this paper, parameters of a given (chaotic) dynamical system are estimated from time series by using identical synchronization between two different systems. This technique is based on the invariance principle of differential equations, i.e., a dynamical Lyapunov function involving synchronization error and the estimation error of parameters. The control used in this synchronization consists of feedback and adaptive control loop associated with the update law of estimation parameters. Our estimation process indicates that one may identify dynamically all unknown parameters of a given (chaotic) system as long as time series of the system are available. Lorenz and Rossler systems are used to illustrate the validity of this technique. The corresponding numerical results and analysis on the effect of noise are also given.     

Synchronization of Modified Chua＇s Circuit with x｜x｜ Function

This paper considers the chaos synchronization of the modified Chua＇s circuit with x｜x｜ function. We firstly show that a couple of the modified Chua systems with different parameters and initial conditions can be synchronized using active control when the values of parameters both in drive system and response system are known aforehand.Furthermore, based on Lyapunov stability theory we propose an adaptive active control approach to make the states of two identical Chua systems with unknown constant parameters asymptotically synchronized. Moreover the designed controller is independent of those unknown parameters. Numerical simulations are given to validate the proposed synchronization approach.     

Effect of discrete time observations on synchronization in Chua model and applications to data assimilation

Data assimilation is a tool, which incorporates observations in the model to improve the forecast, and it can be thought of as a synchronization of the model with observations. This paper discusses results of numerical identical twin experiments, with observations acting as master system coupled unidirectionally to the slave system at discrete time instances. We study the effects of varying the coupling constant, the observational frequency, and the observational noise intensity on synchronization and prediction in a low dimensional chaotic system, namely, the Chua circuit model. We observe synchrony in a finite range of coupling constant when coupling the x and y variables of the Chua model, but not when coupling the z variable. This range of coupling constant decreases with increasing levels of noise in the observations. The Chua system does not show synchrony when the time gap between observations is greater than about one-seventh of the Lyapunov time. Finally, we also note that the prediction errors are much larger when noisy observations are used than when using observations without noise.     

基于模糊观测器的Chua混沌系统投影同步

研究了Chua混沌系统的广义投影同步问题.基于Takagi-Sugeno(T-S)模糊模型,设计了一种模糊观测器.利用Lyapunov稳定性理论证明了所提方案的可行性和全局稳定性.数值仿真试验进一步验证了该方案可以实现Chua混沌系统的投影同步.方法设计简单,且可以推广到其他的一些混沌系统. 关键词： T-S模糊模型 Chua混沌系统 观测器 投影同步     

Synchronization of a class of chaotic systems using small impulsive signal

In this paper the issue of impulsive synchronization of a class of uncertain chaotic systems with parameters perturbation is investigated. Applying the impulsive theory and linear matrix inequality technique, some less conservative and easily verified criteria for impulsive synchronization of chaotic systems are derived. The proposed impulsive synchronization scheme is applied to the chaotic Murali–Lakshmanan–Chua circuit and hyperchaotic Chen and the corresponding synchronization conditions are derived. Moreover, the boundaries of the stable region are also estimated according to the equidistant impulse interval. The effectiveness of the method is demonstrated by the computer simulation.     

Ternary-material lithium-ion battery SOC estimation under various ambient temperature

Ambient temperature produces great effects on battery state-of-charge (SOC) estimation, due to the unstable estimation algorithm, the weakened traceability of battery model, and variable model parameters at various temperatures, especially lower temperatures. The widely used method based on the equivalent circuit model (ECM) offline in using different algorithm, like current integral, the extended Kalman filter (EKF), or the unscented Kalman filter (UKF), can obtain an accurate SOC estimation at room temperature, but it is difficult to guarantee the high precision at lower temperatures. To address this problem, the battery model is investigated at different temperature, and an offset item is proposed to develop the observer equation in the estimated model. Then, the square root of the Sigma points Kalman filter (SR-UKF) is applied, and on the basis of the individual model parameter-temperature table and the developed model, the high accuracy of SOC estimation is achieved. Additionally, considering the burden of original parameter modification (all model parameters modified) at various temperature which will increase the product cost and computational complexity of the battery management system (BMS), the relationship between individual model parameter and the error of SOC estimation is built, which is helpful for the simplification of parameter modification. The results indicate that the proposed method based on the developed estimated model and the simplified parameter modification can achieve an accurate, stable, and efficient SOC estimation.     

基于线性反馈控制的不确定混沌系统的参数辨识

基于线性反馈控制方法和Lyapunov稳定性理论,研究了参数不确定系统的混沌同步和参数辨识,设计了普遍适用的参数自适应控制律,理论证明设计的控制器可使得两个结构相同但参数不同的混沌系统渐近地达到同步,并且可以辨识出响应系统的未知参数. Lorenz系统和蔡氏电路的数值仿真进一步表明了该方法的有效性. 关键词： 不确定混沌系统 同步 参数辨识 Lyapunov函数     

Time-delay feedback control in a delayed dynamical chaos system and its applications

The feedback control of a delayed dynamical system, which also includes various chaotic systems with time delays, is investigated. On the basis of stability analysis of a nonautonomous system with delays, some simple yet less conservative criteria are obtained for feedback control in a delayed dynamical system. Finally, the theoretical result is applied to a typical class of chaotic Lorenz system and Chua circuit with delays. Numerical simulations are also given to verify the theoretical results.     

基于光电负反馈的激光混沌串联同步系统研究

提出了一种新的混沌中继系统，该系统基于光电负反馈的激光混沌串联同步.对该系统的同步性以及激光器内部参数失配对其同步性的影响进行了研究，并与基于光反馈的激光混沌串联同步系统进行了比较.研究结果表明：当系统参量满足一定条件时，该系统能实现完全同步；当激光器内部参数失配时，其混沌同步性会受到一定的影响，但该系统的混沌同步性对参数失配的容忍性要比基于光反馈的系统好得多.     

Estimating model parameters in nonautonomous chaotic systems using synchronization

In this Letter, a technique is addressed for estimating unknown model parameters of multivariate, in particular, nonautonomous chaotic systems from time series of state variables. This technique uses an adaptive strategy for tracking unknown parameters in addition to a linear feedback coupling for synchronizing systems, and then some general conditions, by means of the periodic version of the LaSalle invariance principle for differential equations, are analytically derived to ensure precise evaluation of unknown parameters and identical synchronization between the concerned experimental system and its corresponding receiver one. Exemplifies are presented by employing a parametrically excited 4D new oscillator and an additionally excited Ueda oscillator. The results of computer simulations reveal that the technique not only can quickly track the desired parameter values but also can rapidly respond to changes in operating parameters. In addition, the technique can be favorably robust against the effect of noise when the experimental system is corrupted by bounded disturbance and the normalized absolute error of parameter estimation grows almost linearly with the cutoff value of noise strength in simulation.     

Chaos control by using Motor Maps

In this paper a new method for chaos control is proposed, consisting of an unsupervised neural network, namely a Motor Map. In particular a feedback entrainment scheme is adopted: a chaotic system with a given parameter set generates the reference trajectory for another chaotic system with different parameters to be controlled: the Motor Map is required to provide the appropriate time-varying gain value for the feedback signal. The state of the controlled system is considered as input to the Motor Map. Particular efforts have been paid to the feasibility of the implementation. Indeed, the simulations performed have been oriented to design a Motor Map suitable for an hardware realization, thus some restrictive hypotheses, such as for example a low number of neurons, have been assumed. A huge number of simulations has been carried out by considering as system to be controlled a Double Scroll Chua Attractor as well as other chaotic attractors. Several reference trajectories have also been considered: a limit cycle generated by a Chua's circuit with different parameters values, a double scroll Chua attractor, a chaotic attractor of the family of the Chua's circuit attractors. In all the simulations instead of controlling the whole state space, only two state variables have been fed back. Good results in terms of settling time (namely, the period in which the map learns the control task) and steady state errors have been obtained with a few neurons. The Motor Map based adaptive controller offers high performances, specially in the case when the reference trajectory is switched into another one. In this case, a specialization of the neurons constituting the Motor Map is observed: while a group of neurons learns the appropriate control law for a reference trajectory, another group specializes itself to control the system when the other trajectory is used as a reference. A discrete components electronic realization of the Motor Map is presented and experimental results confirming the simulation results are shown. (c) 2002 American Institute of Physics.     

Synchronization-based approach for parameters identification in delayed chaotic neural networks

In this paper, an adaptive procedure to the problem of synchronization and parameters identification for chaotic neural networks with time-varying delay is introduced by combining the adaptive control and linear feedback with appropriate update law. Based on the invariance principle of functional differential equations, all the connection weight matrices can be efficiently estimated according to a simple, rigorous, and systematic technique. This approach is also able to track the changes in the operating parameters of the experimental neural networks rapidly. The speed of synchronization and parameters estimation can be adjusted under the adaptive gain properly chosen. In addition, the method is simple to implement in practice, and it is quite robust against the effect of slight noise in the given time series and the estimated value of a parameter fluctuates around the correct value.     

Parameter estimation based synchronization for an epidemic model with application to tuberculosis in Cameroon

We propose a method based on synchronization to identify the parameters and to estimate the underlying variables for an epidemic model from real data. We suggest an adaptive synchronization method based on observer approach with an effective guidance parameter to update rule design only from real data. In order, to validate the identifiability and estimation results, numerical simulations of a tuberculosis (TB) model using real data of the region of Center in Cameroon are performed to estimate the parameters and variables. This study shows that some tools of synchronization of nonlinear systems can help to deal with the parameter and state estimation problem in the field of epidemiology. We exploit the close link between mathematical modelling, structural identifiability analysis, synchronization, and parameter estimation to obtain biological insights into the system modelled.     

Impulsive synchronization of chaotic systems

The issue of impulsive synchronization of a class of chaotic systems is investigated. Based on the impulsive theory and linear matrix inequality technique, some less conservative and easily verified criteria for impulsive synchronization of chaotic systems are derived. The proposed method is applied to the original Chua oscillators, and the corresponding synchronization conditions are obtained. Moreover, the boundary of the stable region is also estimated in terms of the equidistant impulse interval. The effectiveness of our method is shown by computer simulation.