非整数阶系统频域辨识的递推最小二乘算法

非整数阶系统辨识方法是建立非整数阶系统模型的一种重要工具．本文提出了一种非整数阶系统频域辨识的最小二乘递推算法．给出了算法的详细推导，并用已知系统验证了算法的有效性．结果表明该算法是整数阶系统辨识的最小二乘递推算法的推广．使用此算法，不但能辨识整数阶系统，还能辨识非整数阶系统．     

非整数阶系统的频域辨识法

提出了一类非整数阶系统的频域辨识最小二乘方法, 给出了算法的详细推导过程. 通过对已知系统仿真, 结果表明该方法有如下优点: 对于非整数阶对象, 能够用更简单的模型获得更好的频域响应拟合; 对于整数阶对象, 采用阶数扫描的方法仍然能找到拟合其频域响应的最好的整数阶模型; 与整数阶系统辨识算法相比, 该算法更稳定.     

非整数阶系统连续时间域的模拟

提出了一种致力于非整数阶系统连续时间域的模拟方法.这种特殊系统的仿真建立在有限频率区间非整数阶积分算子的基础上,其非整数阶作用仅限于有限频率区域.然后,可以定义非整数阶模型的状态方程实现,它允许近似理想非整数阶系统的连续时间响应.最后,通过两个数字仿真例子来验证这种模拟方法.     

不确定混沌系统用分数阶系统同步与参数辨识

针对不确定整数阶混沌系统的同步和参数辨识问题,提出一种新的策略即用分数阶混沌系统来同步整数阶混沌系统并实现不确定参数的辨识。首先引入预控制量并利用主动控制构造同步误差方程,然后用分数阶混沌系统稳定性理论和自适应控制理论,设计同步控制器及参数的自适应率,最终实现整数阶混沌系统用分数阶混沌系统同步和参数辨识。数值仿真实现参数不确定整数阶Lorenz系统用分数阶Lorenz系统进行同步和参数辨识仿真,结果表明提出方法的有效性。     

多变量系统状态空间模型的递阶辨识

研究多变量系统状态空间模型的递阶辨识问题,推广了作者提出的标量系统状态和参数联合辨识算法.当状态可量测时,利用最小二乘原理直接辨识状态空间模型的参数矩阵;当状态不可测时,利用递阶辨识原理提出了状态空间模型递阶辨识方法,使用系统输入输出数据来估计系统的未知状态和参数.状态空间模型递阶辨识方法分为两步:首先假设系统状态是已知的(即参数估计算法中的未知系统状态用其估计代替),基于状态估计和系统输入输出数据递归计算系统参数估计;然后基于系统输入输出数据和获得的参数估计,递归计算系统的状态估计.     

分数阶系统频域辨识算法

分数阶微积分提供了一个很好的工具来描述一些复杂的实际系统,比整数阶模型更简洁准确.针对分数阶系统建模问题,阐述了一种同元次分数阶系统频域辨识的极大似然算法.为此首先简要地介绍了同元次分数阶系统的传递函数表达形式,然后在此基础上推导了分数阶系统频域极大似然算法,利用拉格朗日法证明了似然函数和代价函数的等价性,从而将辨识问题归结为一等价的优化问题,并进一步对采用Gauss-Newton优化计算方法进行了讨论.最后通过仿真实例验证了其有效性.     

复杂分数阶多自主体系统的运动一致性

复杂环境中,许多自然现象的动力学特性不能应用整数阶方程描述,而只能用分数阶（非整数阶）动力学的智能个体合作行为来解释. 本文假设多自主体 系统存在个体差异,采用不同的分数阶动力学特性组成复杂分数混合阶微分方程. 应用分数阶系统的Laplace变换和频域理论,研究了有向网络拓扑下,时延分数混合阶多自主体系统的运动一致性. 由于整数阶系统是分数阶系统的特殊情况,本文的结论可以推广到整数阶与分数阶混合的多自主体系统中. 最后,应用仿真实例对本文结论进行了验证.     

不同阶分数阶混沌系统的同步与参数辨识

针对不确定分数阶混沌系统的同步和参数辨识问题,提出一种新的方法,即用不同阶分数阶系统来同步和参数辨识.利用主动控制和预控制量方法,基于分数阶混沌系统稳定性理论和自适应控制理论,设计控制器,实现不同阶分数阶混沌系统之间的同步和参数辨识.理论和仿真结果实现了不同阶Chen 系统间的同步和辨识,表明了该方法的有效性.     

分数阶非因果BP神经网络模型

由一阶因果、反因果微分的定义推导出Caputo分数阶因果、反因果微积分,并在此基础上定义Caputo分数阶非因果微积分。将它们分别应用于BP神经网络的反向传播过程中对权值进行处理,产生了Caputo分数阶因果、反因果和非因果BP神经网络模型。为了方便对比,将这些模型分别对波士顿房屋数据集和MNIST数据集进行处理。模拟结果表明：在整数阶因果、反因果和非因果的模型之间,整数阶非因果模型的结果最好;分数阶因果、反因果和非因果模型分别与其相应的整数阶模型进行比较,得出分数阶模型得到的准确率比整数阶的高;在分数阶因果、反因果和非因果的模型之间,非因果的准确性最高。总的来说,Caputo分数阶因果、反因果和非因果微积分都对传统BP神经网络有优化作用,尤其是分数阶非因果微积分的优化效果最好。     

一般双率随机系统状态空间模型及其辨识

对于双率采样数据系统,本文使用提升技术,推导了双率系统的提升状态空间模型.对 于系统状态可测量的双率系统,利用最小二乘原理辨识提升系统模型的参数矩阵;对于状态不 可测的未知参数双率系统,利用递阶辨识原理,提出了双率系统递阶状态空间模型辨识方法,来 辨识系统的状态和参数.具体做法:基于获得的状态估计和提升系统的输入输出数据递归估计 系统参数,然后基于获得的参数估计,计算系统的状态.     

Generalized exponential functions for system analysis and synthesis

Generalized exponential functions are defined and developed for the analysis and aynthesis of dynamical systems. Approximation of conventional response transforms in the 8-domain is implemented by utilizing non-integer-order complex operator 8v. The analysis technique is applied to a non-inductive long cable and to network systems. In addition, for the synthesis, ?aguorre-?eo functions in the 8-domain are extended into the non-integer-order 8v-domain in terms of generalized exponential functions. The application is demonstrated by an example     

Compensation method for non-linear systems having jump and hysteresis properties†

The paper introduces a novel method for describing non-linear systems. The method, which uses non-integer calculus provides greater insight into the physical aspects of jump and hysteresis phenomena in systems with single-valued non-linear characteristics. The method is well suited to non-linear systems having minimum phase-shift linear parts. A non-integer-order compensator is developed for singularities arising from non-linearities. To illustrate the scheme, saturation is considered. Numerical design of a compensator for jump and hysteresis is also described.     

基于有源RFID技术的定位系统研究

针对传统出入口监控管理方式的效率低下及无源射频识别技术在实际应用中存在的金属物干扰、人体吸收、识别数量有限等问题,提出一种基于有源2.4 G射频识别技术的出入口监控管理方法。该方法利用125 kHz激励器唤醒2.4 GHz射频识别标签及标签定位的技术,比传统出入口监控管理方式更加准确、便捷、高效;同时,有效地延长了有源射频识别设备的使用寿命。采用低频触发器、读卡器和有源2.4 G射频识别标签等设备,搭建试验环境进行为期30天的测试;结果表明,基于低频触发的有源射频识别出入口监控管理系统具有较高的可靠性和实用性。     

Model identification using tie line power and frequency measurements

The identification of dynamic models which relate power and frequency deviations on a tie line of a power system is investigated. The identification problem is posed and three identification algorithms are presented which produce least squares models with different structural properties. Model order is determined by applying residual and system structure tests to a sequence of models of increasing order. These tests indicate the model order for both equivalent realizations and predictive models. Equivalent realizations are identified on one data set and then their performance as a dynamic equivalent is evaluated on a second data set. These equivalent realizations are also used to predict frequency in an iterative frequency prediction algorithm. Predictive models are also identified and their performance as frequency predictors is evaluated using a direct prediction algorithm. The identification of dynamic equivalents provides information about the structural properties of power systems. The use of dynamic equivalents and predictive models for frequency prediction indicates the tradeoff in accuracy vs the prediction interval which can be obtained using these least squares algorithms and the measurement device presently available.     

A novel whispered speaker identification system based on extreme learning machine

Whispered speech speaker identification system is one of the most demanding efforts in automatic speaker recognition applications. Due to the profound variations between neutral and whispered speech in acoustic characteristics, the performance of conventional speaker identification systems applied on neutral speech degrades drastically when compared to whisper speech. This work presents a novel speaker identification system using whispered speech based on an innovative learning algorithm which is named as extreme learning machine (ELM). The features used in this proposed system are Instantaneous frequency with probability density models. Parametric and nonparametric probability density estimation with ELM was compared with the hybrid parametric and nonparametric probability density estimation with Extreme Learning Machine (HPNP-ELM) for instantaneous frequency modeling. The experimental result shows the significant performance improvement of the proposed whisper speech speaker identification system.     

An Analytic Geometry Approach to Wiener System Frequency Identification

This paper addresses the problem of Wiener system identification. The underlying linear subsystem is stable but not necessarily parametric. The nonlinear element in turn is allowed to be nonparametric, noninvertible, and nonsmooth. As Wiener models are uniquely defined up to an uncertain multiplicative factor, it makes sense to start the frequency identification process estimating the system phase (which is common to all models). To this end, a consistent estimator is designed using analytic geometry tools. Accordingly, the system frequency behavior is characterized by a family of Lissajous curves. Interestingly, all these curves are candidates to modelling the system nonlinearity, but the most convenient one is the less spread of them. Finally, the frequency gain is in turn consistently estimated optimizing an appropriate cost function involving the obtained phase and nonlinearity estimates.     

A study of frequency prediction for power systems

A frequency predictor is identified from simulated measurements of power and frequency on a power system. An on-line Ieast-squares algorithm is used along with a new system structure test for model order identification. A comparison of this system structure test with other model order identification tests is also included. The performance of the resultant predictor is then determined as a function of both the prediction interval and the sampling rate and measurement noise levels on the power and frequency measurements used for the predictor. The results indicate an increase in prediction error with the length of the prediction interval because the predictor loses its principal dependence of "P-f" (power-frequency) dynamics in the power system and depends more strongly on the random load fluctuations over the prediction interval. The modeling error was shown to be unaffected by sampling rate and by measurement noise levels below that of the present power-frequency recorder [2], but was affected by measurement noise levels above the values on the present recorder. This accuracy of the model for small prediction intervals justifies the future use of frequency measurements in power system identification and justifies the use of least-squares algorithms using these measurements. The results on sampling rate and measurement noise imply that the present recorder [2] is an "optimal" design and that the RTDAS [5] will be an even better tool for use in power system model identification.     

Hierarchical multi-criteria risk-benefit analysis in fuzzy environment

The objectives of this paper are: (1) to utilize hierarchical fuzzy technique for order preference by similarity to ideal solution (TOPSIS) to evaluate the most suitable RFID-based systems decision and (2) to list key risks and benefits of radio frequency identification technology in library services. Researcher explains the importance of selection criteria for evaluation of RFID-based systems. It provides key elements on radio frequency identification, fuzzy hierarchical TOPSIS methodology and an algorithm that can be followed to solve the problem. A sample problem using the algorithm is solved and results are explained. Due to the fact that a better management of a library system is related to the full understanding of the technologies implemented and the system under consideration, some background on the radio frequency identification technology is provided and the kind of systems that most likely managements would face with and select are provided.     

无模型SISO时滞系统的PID参数稳定域研究

针对无精确模型的单输入单输出(SISO)时滞系统, 利用频率响应数据, 给出确定PID参数稳定域的解析方法. 首先通过继电反馈频域辨识法得到系统的频率响应数据, 然后基于该数据确定控制参数的奇异边界线和非奇异边界线, 并判断边界线的哪一侧具有更少的不稳定极点, 从而给出能使闭环系统稳定的控制参数区域. 该方法避免了模型辨识的复杂计算过程, 为无模型SISO时滞系统的PID控制器设计和调节提供了一条简单有效的途径. 仿真结果验证了方法的有效性.