基于GPEM主旋律分析的系统序参量识别方法研究

针对系统的演化与发展,引入基于目标规划评价模型(GPEM)的主旋律分析方法,识别引导与支配系统发展的序参量.通过分析主旋律与序参量间的关系,认为在GPEM中的价值参数结构是二者的共同体现,且主旋律分析是序参量识别的前提与基础.应用基于GPEM的主旋律分析方法对系统所属群体进行相关计算,可以获得群体发展的主旋律,同时可以获得群体成员在各主旋律下的排名向量,进而认为与理想排名向量相似系数最高的主旋律在群体发展过程中发挥着关键性作用,则该主旋律对应的价值参数结构即为序参量.通过观察该主旋律所集聚成员在理想排名向量中的位置,来判断与分析序参量的特征.     

基于数据包络分析的并行生产系统效率评价方法

数据包络分析(DEA)是一种评价具有多投入、多产出决策单元的相对效率的线性规划方法.在现实世界中,决策单元有时呈现出由多个独立子系统构成的复杂并联网络系统,各子系统的投入/产出之和构成了系统的总投入/产出.目前,用于评价这种具有并联网络生产系统相对效率的模型主要有三种:网络DEA模型、多部门DEA模型和关联DEA模型.现有这些模型的基本特性和相互关系存在着不足,即子系统的效率分解和优化指数不唯一.为解决这一问题,提出了改进的并联DEA模型,并采用加拿大银行系统实例来说明所提出模型的合理性和有效性.     

多投入多产出效率测量与影响因素判断

多投入多产出效率的测量是极为重要的,而更为重要的是判断究竟是什么因素影响了效率,因为后者能为科学决策提供有力依据。本文提出按效率与影响因素相关系数最大化来确定投入与产出的权重,从而计算效率的方法,并同时识别了影响因素。本文还用该方法给出了一个算例,其结论颇有启发。结果表明,该方法对于多投入、多产出的效率问题的测量与多影响因素的判断相当有效。     

低碳供应链协同运作的演化模型

针对低碳供应链协同运作的系统演化问题,运用协同论中的伺服原理分析了低碳供应链中的序参量和运作流程;运用布尔代数的逻辑加和逻辑乘运算规则,分析了序参量和运作流程之间的关系。在此基础上,根据系统动力学中的速率原理,构建了低碳供应链协同运作的演化模型,运用自组织原理分析了序参量影响低碳供应链协同运作的演化过程,运用协同效应原理分析了低碳供应链协同运作规律,并对模型进行了应用仿真分析,得出了低碳供应链协同运作的机理。     

基于“零和收益”的SBM效率分配方法及应用

在松弛变量度量(slacks-based measure,SBM)效率评价方法的基础上,首先明确投入(产出)要素固定的生产系统中,投入(产出)要素在各决策单元间的竞争性关系;然后采用比例分配策略对SBM无效决策单元的投入(产出)松弛进行效率分配,以构建一个基于零和收益的SBM(zero sum gains SBM,ZSG-SBM)效率分配方法;再通过分析ZSG-SBM模型与SBM模型效率评价结果的关系,给出了比例分配策略ZSG-SBM模型的求解方法;最后应用实例研究验证了本文模型在要素存在竞争性的复杂生产系统效率评价和资源分配中的优势。     

非期望产出的DEA效率评价

将非期望产出作为投入应用到传统DEA模型上,解决了非期望产出生产活动的效率评价问题.结合生产可能集,将非期望产出直接反映到生产可能集中,建立了基于投入导向的径向和非径向两种DEA模型.并对两种DEA模型效率值的大小关系、相对有效性的等价性问题进行了证明,指出非径向DEA模型更能准确的实现效率定量评价.     

广义超效率区间DEA模型及其有效性研究

根据样本单元的区间投入、区间产出定义最大样本生产可能集,建立基于最大样本生产可能集的广义超效率区间DEA模型,然后定义了待评价决策单元基于广义超效率区间DEA模型的超效率区间,并讨论了待评价决策单元的有效性,最后通过实例表明了广义超效率区间DEA模型的实用性.     

基于DEA方法和粗糙集的政府效率评估模型

政府效率影响政府的执政能力。评价政府效率必须考虑投入和产出之间的关系。本文首先利用DEA方法建立投入一产出多指标模型评价政府工作的相对有效性，并对评价结果进行离散化处理；然后运用粗糙集方法对离散后的数据进行分析，得出每个待评对象的综合评分。两种方法的有机结合，使得建立的政府效率评价模型既能充分反映政府效率投入一产出的特点，又能有效避免人为因素对模型的影响，以得到更合理的评估结果。     

基于DEA的竞争战略识别

针对有效决策单元评价和区分的问题,在充分提取决策单元之间相似性和相异性信息基础上,定义了多指标区间交叉效率,进而提出了一种基于投入、产出权重的聚类分析方法,并将其应用于竞争战略识别.实证结果表明,该方法能够区分有效决策单元,综合评价具有统一性和合理性;与同类战略识别方法相比,更具客观性和解释能力,分类效果更好.该方法提供了一种客观的新的竞争战略识别方法,有助于战略有效性的研究.     

基于电能节约的E-DEA模型及其应用

按照全要素能源效率的概念,重点考虑电能投入约束,构造了基于电能节约的E-DEA模型,其目标函数为极大化产出比例和电能投入比例之差,约束条件中除考虑一般投入量约束外,还同时强调电能投入径向节约和产出径向增加。根据模型最优解,给出了相应的有效、非有效、弱有效、用电规模收益状态的判断准则,以及相应于不同有效性情况下决策单元的改进。以合肥市通用制造业规上企业所属21个行业为研究对象,从第二次经济普查中选择年均资产、从业人员、电力、非电力能源、二氧化碳排量为投入指标,主营业务收入为产出指标,对行业电能利用效率进行实证分析,通过分析潜在电能可节约量和主营业务收入可增加量,明确了各行业改进目标。     

Auxiliary model based least squares identification method for a state space model with a unit time-delay

This paper considers parameter estimation problems for state space systems with time-delay. By means of the property of the shift operator, the state space systems are transformed into the input–output representations and an auxiliary model identification method is presented to estimate the system parameters. Finally, an example is provided to test the effectiveness of the proposed algorithm.     

A least squares identification algorithm for a state space model with multi-state delays

A parameter estimator is presented for a state space model with time delay based on the given input–output data. The basic idea is to expand the state equations and to eliminate some state variables, and to substitute the state equation into the output equation to obtain the identification model which contains the information vector and parameter vector. A least squares algorithm is developed to estimate the system parameter vectors. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithm.     

Identification of nonlinear system using extreme learning machine based Hammerstein model

In this paper, a new method for nonlinear system identification via extreme learning machine neural network based Hammerstein model (ELM-Hammerstein) is proposed. The ELM-Hammerstein model consists of static ELM neural network followed by a linear dynamic subsystem. The identification of nonlinear system is achieved by determining the structure of ELM-Hammerstein model and estimating its parameters. Lipschitz quotient criterion is adopted to determine the structure of ELM-Hammerstein model from input–output data. A generalized ELM algorithm is proposed to estimate the parameters of ELM-Hammerstein model, where the parameters of linear dynamic part and the output weights of ELM neural network are estimated simultaneously. The proposed method can obtain more accurate identification results with less computation complexity. Three simulation examples demonstrate its effectiveness.     

Combined separable signals based neuro-fuzzy Hammerstein–Wiener model

Hammerstein–Wiener model can describe a large number of complicated industrial processes. In this paper, a novel identification method for neuro-fuzzy based Hammerstein–Wiener model is presented. A neuro-fuzzy system with correlation analysis based non-iterative parameter updating algorithm is proposed to model the static nonlinearity of Hammerstein–Winer processes. As a result, the proposed method not only avoid the inevitable restrictions on static nonlinear function encountered by using the polynomial approach, but also overcomes the problems of initialization and convergence of the model parameters, which are usually resorted to trial and error procedure in the existing iterative algorithms used for the identification of Hammerstein–Winer model. In addition, combined separable signals are adopted to identify the Hammerstein–Wiener process, resulting in the identification problem of the linear model separated from that of nonlinear parts. Moreover, one part of the input signals is extended to more general signals, such as binary signals, Gaussian signals or other modulated signals. Examples are used to illustrate the effectiveness of the proposed method.     

A low-order active fault-tolerant state space self-tuner for the unknown sampled-data nonlinear singular system using OKID and modified ARMAX model-based system identification

In this paper, we present two control schemes for the unknown sampled-data nonlinear singular system. One is an observer-based digital redesign tracker with the state-feedback gain and the feed-forward gain based on off-line observer/Kalman filter identification (OKID) method. The presented control scheme is able to make the unknown sampled-data nonlinear singular system to well track the desired reference signal. The other is an active fault tolerance state-space self-tuner using the OKID method and modified autoregressive moving average with exogenous inputs (ARMAX) model-based system identification for unknown sampled-data nonlinear singular system with input faults. First, one can apply the off-line OKID method to determine the appropriate (low-) order of the unknown system order and good initial parameters of the modified ARMAX model to improve the convergent speed of recursive extended-least-squares (RELS) method. Then, based on modified ARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown sampled-data nonlinear singular system with immeasurable system state. Moreover, in order to overcome the interference of input fault, one can use a fault-tolerant control scheme for unknown sampled-data nonlinear singular system by modifying the conventional self-tuner control (STC). The presented method can effectively cope with partially abrupt and/or gradual system input faults. Finally, some illustrative examples including a real circuit system are given to demonstrate the effectiveness of the presented design methodologies.     

Parameter identification of Rossler’s chaotic system by an evolutionary algorithm

In this paper, a differential evolution (DE) algorithm is applied to parameter identification of Rossler’s chaotic system. The differential evolution has been shown to possess a powerful searching capability for finding the solutions for a given optimization problem, and it allows for parameter solution to appear directly in the form of floating point without further numerical coding or decoding. Three unknown parameters of Rossler’s Chaotic system are optimally estimated by using the DE algorithm. Finally, a numerical example is given to verify the effectiveness of the proposed method.     

A receding horizon D-optimization approach for model identification–oriented input design and application in combustion engines

In this paper, a receding horizon D-optimization approach for model identification–oriented input design is proposed, and a practical application is demonstrated for internal combustion engines. The proposed approach consists of a recursive parameter identification algorithm and an input signal design algorithm; where the latter provides D-optimal excitation signal for the adaptation of the parameter estimation in the following identification phase. The D-optimization algorithm is constructed with the Continuation/GMRES method, which provides an approximate solution according to the current parameter of the model. To validate effectiveness and feasibility of the proposed approach, testing results applying the proposed approach to an internal combustion engine are demonstrated and conducted on a full-scale engine test bench.     

飞艇姿态跟踪系统的研究

研究了具有参数不确定和外部干扰的飞艇姿态跟踪控制问题．飞艇姿态运动的数学模型为一个多输入/多输出不确定非线性系统,根据该系统的特点,采用了一个基于不确定项上界的鲁棒输出跟踪控制器设计方法,应用输入/输出反馈线性化法和李雅普诺夫方法,设计了飞艇姿态鲁棒控制律,它可确保系统输出按指数规律跟踪期望输出．该控制器设计简单,易于实现．仿真结果表明:即使系统存在不确定性和外界干扰,仍可在闭环系统中实现精确的姿态控制．     

基于信息压缩矩阵特征值映射的时变系统UD分解辨识算法

在系统辨识领域遗忘因子UD分解算法(一种通过对系统数据矩阵进行UD分解的在线辨识算法)具有对时变系统阶次和参数同步估计的优异性能,但传统的遗忘策略不能从根本上解决信息压缩矩阵数据过饱和问题,为了拓展现有UD分解算法在时变系统的适用范围,同时针对数据空间分布不均匀性,提出一种基于信息压缩矩阵特征值映射的UD分解辨识算法.从理论上分析辨识算法跟踪能力与参数估计矩阵有界性的对应关系,从而构造出一种基于信息压缩矩阵特征值映射的有界函数,特征值映射函数能够根据系统数据传递过程中信息量的大小动态调整遗忘因子,解决了参数辨识过程中数据过饱和及数据分布不均匀问题.仿真结果表明,相比于常规时变遗忘因子策略,带有特征值映射的UD分解算法能够更加准确跟踪系统参数的变化,且能够保证系统不是2N阶持续激励信号的情况下,也能对时变系统参数进行跟踪.