一种新的基于遗忘因子的递推子空间辨识算法

针对工业系统中广泛存在的时变特性, 提出一种新的递推子空间辨识算法, 实现对系统状态空间模型的在线递推估计. 为更好地跟踪系统时变特性, 研究基于遗忘因子的输入输出数据矩阵构造机制, 以提高递推算法的收敛速度; 针对算法中奇异值分解的求解问题, 将梯度型算法引入基于遗忘因子的状态子空间跟踪中, 实现对广义能观测矩阵的估计, 避免了子空间近似带来的估计有偏性; 该算法计算简单有效, 且对初值具有更高的鲁棒性; 最后给出该递推算法的性能分析, 理论证明其收敛性, 并通过仿真实例验证算法的有效性.     

一种新型高精度温度控制方法

为控制环境温度剧烈变化,对于精密仪器设备对温度精度和系统稳定性要求也随之提高,提出了一种参数自学习比例-周期控温算法,能够自动辨识系统参数和控制参数,并具有自学习能力。当环境温度快速变化时,可以采用相应的控制策略,达到理想的控制效果,适用于控制系统参数未知的场合,同时对环境温度的变化具有较强的自适应能力。仿真结果表明,算法控制精度高、自适应能力强,确实能够满足精密仪器设备对温度的精度和稳定性的要求。     

基于贝叶斯网络的复杂装备D-S诊断

利用D-S证据理论进行故障诊断时,首先需要确定出故障识别框架和基本置信分配.但在导弹武器等复杂装备中,存在着故障原因与故障征兆之间关联关系不确定的现象,增加了求取故障识别框架和基本置信分配的困难.文中提出了利用贝叶斯网络理论来解决难题的方法,使D-S证据理论能在复杂装备故障诊断中得到应用.文中首先给出了诊断贝叶斯网络的适应度函数,并利用改进的粒子群优化算法求解了诊断贝叶斯网络.然后分析了诊断贝叶斯网络中故障原因与故障征兆间的关联关系,给出了征兆集合、异因征兆集合概念.提出了求解故障识别框架和基本置信分配的方法.最后,通过实例的D-S诊断,验证了所提方法的正确性.     

基于子空间方法的精馏塔系统辨识建模研究

子空间辨识方法作为一种有效的针对多输入-多输出系统(MIMO)的辨识建模方法近年来受到广泛的重视.目前主要采用的子空间辨识算法只能适用于白噪声环境,而实际的工业现场数据很多是受到较大有色噪声干扰的.针对问题采用了一种新的子空间辨识算法,利用马尔可夫参数用于处理随机性部分,同时引入辅助变量用以去除噪声的干扰,能够适用于存在较大有色噪声干扰情况下的辨识建模,并可得到对象的无偏模型,建模的精度优于通常所采用的子空间辨识算法.通过对精馏塔仿真模型的辨识结果证明了该方法的可行性和有效性,以及在实际工业过程对象建模中良好的应用前景.     

基于模糊专家规则的离散事件辨识及其在交通诱导中的应用

传统离散事件动态系统中的离散事件状态的转换具有不确定性,其不确定性主要来自状态转向发生时刻的不确定性,而所转向的状态一般具有确定性。本文对具有状态转向时刻和转向状态的二重不确定性的离散事件动态系统进行了讨论,用模糊专家系统来对未来状态和状态迁移时刻二重不确定性的离散事件动态系统进行评价,并以智能交通系统中车辆诱导技术为例,说明了此类模糊专家规则的应用价值。本文提出了一种模拟淬火算法,通过模拟物质加温后急剧冷却的过程来求得目标函数的局部极值,以模拟淬火算法的转向概率作为模糊专家系统中的规则选择概率。该方法有效地保证了事件转换的实时性,提高了交通疏导的效率。     

高/低通复合多通道系统辨识的测试信号设计

从频域的角度研究了二进制多频序列(Muhifrequency Binary Sigal).从该信号的基本特点入手,通过对信号变化疏密的设计,调整信号的频谱分布.当已知待测系统的频谱集中在某些频段时,选取适当的二进制多频序列信号,能使测试信号的频谱也集中在那些频段,能充分地利用测试信号.大大提高了信噪比.设计出高频和低频复合的二进制多频序列信号.应用于一个通道是高通的,另一个通道是低通的二输入系统,能同时充分激励2个通道,取得较好的辩识效果.仿真算例可以看出有效性.     

Smart experience-oriented customer requirement analysis for smart product service system: A novel hesitant fuzzy linguistic cloud DEMATEL method

Smart product service system (PSS) has become an essential strategy to transform towards digital servitization for manufacturing companies. By leveraging smart capabilities, smart PSS aims to create superior user experience in a smart context. To develop a successful smart PSS, customer requirement management from smart experience perspective is necessary. However, it is a challenging task to identify and evaluate diverse, implicit and interrelated smart experience-oriented customer requirement (SEO-CR) in smart PSS context. Hence, this paper proposes an effective methodology to elicit and analyze SEO-CRs. At first, a generic, two-dimensional SEO-CR system is presented as a basis to derive the tailored SEO-CRs for various smart PSS applications. Second, a novel HFLC-DEMATEL (hesitant fuzzy linguistic cloud-based Decision-making and trial evaluation laboratory) method is proposed to accurately evaluate the priority and complicated interaction of SEO-CRs, considering the hesitancy, fuzziness and randomness under uncertain decision environment. Some new operations (e.g., cloud total-relation matrix and weight determination method) and a cloud influence relation map are developed to fully take advantage of cloud model in DEMATEL implementation. Finally, a real case of smart vehicle service system (SVSS) is presented. The 18 SEO-CRs of the SVSS are derived based on the generalized SEO-CRs. By using HFLC-DEMATEL, some important SEO-CRs in context of SVSS are identified, such as autonomous and convenience. The finding of results can help designers make proper decisions in design and development of SVSS with a superior smart experience. The effectiveness and reliability of the proposed method are validated by conducting some comparative analyses.     

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.     

Numerical identification of a nonlinear diffusion coefficient by discrete mollification

The discrete mollification method is a convolution-based filtering procedure suitable for the regularization of ill-posed problems. Combined with explicit space-marching finite difference schemes, it provides stability and convergence for a variety of coefficient identification problems in linear parabolic equations. In this paper, we extend such a technique to identify some nonlinear diffusion coefficients depending on an unknown space dependent function in one dimensional parabolic models. For the coefficient recovery process, we present detailed error estimates and to illustrate the performance of the algorithms, several numerical examples are included.     

Dynamical systems identification using Gaussian process models with incorporated local models

Gaussian process (GP) models form an emerging methodology for modelling nonlinear dynamic systems which tries to overcome certain limitations inherent to traditional methods such as e.g. neural networks (ANN) or local model networks (LMN).The GP model seems promising for three reasons. First, less training parameters are needed to parameterize the model. Second, the variance of the model's output depending on data positioning is obtained. Third, prior knowledge, e.g. in the form of linear local models can be included into the model. In this paper the focus is on GP with incorporated local models as the approach which could replace local models network.Much of the effort up to now has been spent on the development of the methodology of the GP model with included local models, while no application and practical validation has yet been carried out. The aim of this paper is therefore twofold. The first aim is to present the methodology of the GP model identification with emphasis on the inclusion of the prior knowledge in the form of linear local models. The second aim is to demonstrate practically the use of the method on two higher order dynamical systems, one based on simulation and one based on measurement data.