基于BP网络的模糊Petri网的学习能力

模糊Petri网(Fuzzy Petri Nets，FPN)是基于模糊产生式规则的知识库系统的良好建模工具，但自学习能力差是模糊系统本身的一个缺点．该文提出了适合模糊Petri网模型自学习的模糊推理算法和学习算法．在模糊推理算法中，通过对没有回路的FPN模型结构进行层次式划分以及建立变迁点燃和模糊推理的近似连续函数，从而把神经网络中的BP网络算法自然地引入到FPN模型中．在FPN模型上，用误差反传算法计算一阶梯度的方法对模糊产生式规则中的参数进行学习和训练．经过学习和训练的FPN具有很强的泛化能力和自适应功能．FPN模型经过训练得到的参数是有特定含义的，可以通过对这些参数的合法性分析，使得模糊产生式规则系统更加有效，也对知识库系统的建立、更新和维护有着重要的意义．     

基于Sugeno型神经模糊系统的交通流状态预测算法

从交通流状态的模糊特性出发,设计基于Sugeno型神经模糊系统的交通流状态预测算法.选择交通流状态的影响指标作为模糊推理系统的输入、交通流状态作为输出;据经验对输入、输出划分模糊子集,给出相应的隶属度函数并制定模糊规则;建立具有5层结构的神经模糊推理系统,利用神经网络优化调整模糊推理系统的隶属度函数和模糊规则.仿真实验表明,神经网络可直接优化模糊推理系统的隶属度函数,通过对连接权值的训练间接优化模糊规则,故Sugeno型神经模糊系统相比常规模糊系统具有更好的交通流状态预测性能.     

基于分散模糊推理的多输入多输出系统预测控制

对于动态过程具有明显迟延和惯性的MIMO系统,常规模糊控制难以建立模糊规则,控制效果不理想.针对MIMO控制对象,提出一种基于分散模糊推理的预测控制(predictive control based on decentralized fuzzy inference, DFIPC)方法.构造一组与被控输出相对应的分散模糊推理模块,每个推理模块利用一组分散的模糊推理单元,分别根据各个输出的期望值与预测值之间的偏差进行分散推理.在时间层面,根据动态响应程度对推理结果进行加权综合,获得等效控制输入;进一步,通过对等效控制输入加权综合产生系统实际控制输入增量,从而有效克服模糊推理系统处理多维输入信息时模糊规则难以建立的困难.最后,通过实验验证所提出控制方法对于迟延和惯性明显的MIMO控制对象的有效性和适应性.     

基于Takagi—Sugeno的再励学习模糊神经网络控制

提出一种模糊神经网络的自适应控制方案。针对连续空间的复杂学习任务，提出了一种竞争式Takagi—Sugeno模糊再励学习网络，该网络结构集成了Takagi-Sugeno模糊推理系统和基于动作的评价值函数的再励学习方法。相应地，提出了一种优化学习算法，其把竞争式Takagi-Sugeno模糊再励学习网络训练成为一种所谓的Takagi-Sugeno模糊变结构控制器。以一级倒立摆控制系统为例．仿真研究表明所提出的学习算法在性能上优于其它的再励学习算法。     

永磁直线同步电机位置系统的模糊IP控制研究

针对具有电磁推力大、响应快、易于矢量解耦控制的永磁直线同步电机PMLSM,研究高精度位置伺服控制系统的设计,以满足高速加工与高精度微进给加工的需求;考虑被控对象的变化和外界扰动,控制器的参数难于在线修订,设计了一种模糊/积分-比例IP位置控制器;它将具有并联反馈环节的IP控制器与模糊控制器有效结合,根据位置偏差的变化率进行切换,即存在较大输入指令与系统输出偏差较大时采用模糊控制,而系统输出接近于输入指令时则采用IP控制器,从而发挥模糊控制器对变参数系统的自适应性和IP控制器的快速和准确性优势;仿真实验结果表明模糊/IP控制器在稳态精度和动态性能方面优于单纯的IP控制器和模糊控制器,能够满足变参数控制系统的性能指标。     

模糊自适应PID算法在磁悬浮实时控制系统中的应用研究

针对磁悬浮系统的复杂非线性及模型不确定的特点,采用模糊PID算法对其进行控制,以满足系统对动态性能和静态性能的要求;结合PID实时控制中的经验,建立合理的模糊规则,模糊推理机构根据不同的偏差e、偏差变化率ec对PID参数Kp、Ki和Kd进行自校正;在磁悬浮实验装置中进行实时控制实验,通过与常规PID控制效果的比较来验证模糊PID控制器的性能;在系统输入存在正弦扰动时,模糊PID控制器使系统响应过程中的振荡幅度得到明显减小,干扰对控制效果的影响被减弱;实验证明,模糊PID控制器具有较强的鲁棒性和抗干扰能力,对于磁悬浮这种非线性系统具有良好的控制效果。     

基于Takagi-Sugeno的再励学习模糊神经网络控制

提出一种模糊神经网络的自适应控制方案。针对连续空间的复杂学习任务,提出了一种竞争式Takagi-Sugeno模糊再励学习网络,该网络结构集成了Takagi-Sugeno模糊推理系统和基于动作的评价值函数的再励学习方法。相应地,提出了一种优化学习算法,其把竞争式Takagi-Sugeno模糊再励学习网络训练成为一种所谓的Takagi-Sugeno模糊变结构控制器。以一级倒立摆控制系统为例,仿真研究表明所提出的学习算法在性能上优于其它的再励学习算法。     

基于ANFIS的交通事件持续时间预测

针对高速公路交通事故引发交通堵塞的问题,提出一种基于减法聚类和自适应神经模糊推理系统的事件持续时间预测新方法。将该方法应用于交通事件持续时间预测,从I-880数据库中提取事件持续时间相关因素,使用非参数估计法进行显著性分析,将影响程度最大的因素作为模糊系统的输入样本,采用减法聚类对输入样本进行聚类,得到模糊规则数并建立初始模糊推理系统,使用BP反向传播算法和最小二乘估计算法的混合算法对该模糊系统进行训练并优化,建立最终模糊模型。仿真结果证明,该系统对交通事件持续时间预测具有较高检测率和较低误报率。     

一种模糊逻辑系统的快速学习算法

本文提出了一种模糊逻辑系统的快速学习算法．算法要求预先确定各输入变量上模 糊集合的数目及分布;模糊规则前件可以是任意形状的模糊集合,后件则必须采用单值模糊 集合;模糊推理采用乘积推理;解模糊方法采用Tsukamoto方法．算法由输入－输出数据对 提取模糊规则．模糊规则的后件采用最小二乘方法一次计算得出．本算法对目标对象的逼近 精度取决于输入参数上模糊集合的数目,数目越多,精度越高．算法所需计算量小．     

基于T-S模糊模型的非线性噪声消除

噪声是影响系统辨识的不利因素,而实际系统不可避免的受到噪声的污染.对模糊推理系统在噪声消除中的应用进行了研究,提出了一种基于T-S模糊模型的模糊非线性噪声消除算法.说明了非线性噪声消除(NNC)的结构和使用NNC进行噪声抵消的原理.该方法由输入-输出数据对直接提取模糊规则,模糊规则的后件参数采用递推最小二乘法一次计算得出,然后从测量信号中消去噪声得到有用的信号.仿真结果表明模糊推理系统可以应用于噪声消除.     

Robust control for electric vehicle powertrains

This paper considers the application of robust control methods ($\mu$- and ${\rm H}_{\infty}$-synthesis) to the speed and acceleration control problem encountered in electric vehicle powertrains. To this end, we consider a two degree of freedom control structure with a reference model. The underlying powertrain model is derived and combined into the corresponding interconnected system required for $\mu$- and ${\rm H}_{\infty}$-synthesis. The closed-loop performance of the resulting controllers are compared in a detailed simulation analysis that includes nonlinear effects. It is observed that the $\mu$-controller offers performance advantages in particular for the acceleration control problem, but at the price of a high-order controller.     

Event-triggered state estimation for T-S fuzzy affine systems based on piecewise Lyapunov-Krasovskii functionals

This paper investigates the problem of event-triggered ${\rm H}_\infty$ state estimation for Takagi-Sugeno (T-S) fuzzy affine systems. The objective is to design an event-triggered scheme and an observer such that the resulting estimation error system is asymptotically stable with a prescribed ${\rm H}_{\infty}$ performance and at the same time unnecessary output measurement transmission can be reduced. First, an event-triggered scheme is proposed to determine whether the sampled measurements should be transmitted or not. The output measurements, which trigger the condition, are supposed to suffer a network-induced time-varying and bounded delay before arriving at the observer. Then, by adopting the input delay method, the estimation error system can be reformulated as a piecewise delay system. Based on the piecewise Lyapunov-Krasovskii functional and the Finsler''s lemma, the event-triggered ${\rm H}_{\infty}$ observer design method is developed. Moreover, an algorithm is proposed to co-design the observer gains and the event-triggering parameters to guarantee that the estimation error system is asymptotically stable with a given disturbance attenuation level and the signal transmission rate is reduced as much as possible. Simulation studies are given to show the effectiveness of the proposed method.     

Stability and weighted sensitivity analysis of robust controller for heat exchanger

This study presents a parametric system identification approach to estimate the dynamics of a chemical plant from experimental data and develops a robust PID controller for the plant. Parametric system identification of the heat exchanger system has been carried out using experimental data and prediction error method. The estimated model of the heat exchanger system is a time-delay model and a robust PID controller for the time-delayed model has been designed considering weighted sensitivity criteria. The mathematical background of parametric system identification, stability analysis, and ${{\rm H}_\infty }$ weighted sensitivity analysis have been provided in this paper. A graphical plot has been provided to determine the stability region in the $( {{K_{\rm p}},{K_{\rm i}}} )$, $( {{K_{\rm p}},{K_{\rm d}}} )$ and $( {{K_{\rm i}},{K_{\rm d}}} )$ plane. The stability region is a locus dependent on parameters of the controller and frequency, in the parameter plane.     

基于量子计算的粗糙集核属性求解算法

粗糙集的核属性求解问题在经典计算中是一个NP问题.现有的方法中最优的时间复杂度也需要${\rm O}$$ \left(|C||U|\right)$($U$为论域、$C$为属性列数).由于量子计算的并行性特点, 本文致力于采用量子计算的方法来求解粗糙集的核属性, 拟提出了一种基于量子计算的粗糙集核属性求解算法.经过仿真实验, 在任何情况下, 该算法都能以1的总概率得到目标分量; 且通过理论分析证明了算法的时间复杂度不会高于${\rm O}$$\left(|\frac{{\rm{ \mathsf{ π}}}}{2\arcsin\sqrt {\frac{M}{C}}}+1||U|\right)$.     

Quantum Error Correction of Time-correlated Errors

The complexity of the error correction circuitry forces us to design quantum error correction codes capable of correcting a single error per error correction cycle. Yet, time-correlated error are common for physical implementations of quantum systems; an error corrected during the previous cycle may reoccur later due to physical processes specific for each physical implementation of the qubits. In this paper, we study quantum error correction for a restricted class of time-correlated errors in a spin-boson model. The algorithm we propose allows the correction of two errors per error correction cycle, provided that one of them is time-correlated. The algorithm can be applied to any stabilizer code when the two logical qubits and are entangled states of 2 ⁿ basis states in .      

H∞ position transfer and regulation for floating offshore wind turbines

This paper proposes ${\rm H}_\infty$ controller design for platform position transfer and regulation of floating offshore wind turbines. The platform movability of floating wind turbines can be utilized in mitigating the wake effect in the wind farm, thereby maximizing the wind farm''s total power capture and efficiency. The controller is designed so that aerodynamic force is adjusted to meet the three objectives simultaneously, that is, 1) to generate the desired electrical power level, 2) to achieve the desired platform position, and 3) to suppress the platform oscillation. To acquire sufficient aerodynamic force to move the heavy platform, the pitch-to-stall blade pitching strategy is taken instead of the commonly-used pitch-to-feather strategy. The desired power level is attained by the standard constant-power strategy for the generator torque, while ${\rm H}_\infty$ state-feedback control of blade pitch and nacelle yaw angles is adopted for the position regulation and platform oscillation suppression. Weighting constants for the ${\rm H}_\infty$ controller design are adjusted to take the trade-off between the position regulation accuracy and the platform motion reduction. To demonstrate the efficiency of the proposed controller, a virtual 5-MW semi-submersible wind turbine is considered. Simulation results show that the designed ${\rm H}_\infty$ controller successfully accomplishes the platform position transfer and regulation as well as the platform oscillation reduction against wind and wave disturbances, and that it outperforms a previously-proposed linear quadratic controller with an integrator.     

Error quantification of the normalised right graph symbol for an errors-in-variables system

This paper proposes a novel method to quantify the error of a nominal normalized right graph symbol (NRGS) for an errors-in-variables (EIV) system corrupted with bounded noise. Following an identification framework for estimation of a perturbation model set, a worst-case v-gap error bound for the estimated nominal NRGS can be first determined from \textit{a priori} and \textit{a posteriori} information on the underlying EIV system. Then, an NRGS perturbation model set can be derived from a close relation between the v-gap metric of two models and ${\rm H}_\infty$-norm of their NRGSs' difference. The obtained NRGS perturbation model set paves the way for robust controller design using an ${\rm H}_\infty$ loop-shaping method because it is a standard form of the well-known NCF (normalized coprime factor) perturbation model set. Finally, a numerical simulation is used to demonstrate the effectiveness of the proposed identification method.     

Approximation Algorithms for Quickest Spanning Tree Problems

Let $G=(V,E)$ be an undirected multigraph with a special vertex ${\it root} \in V$, and where each edge $e \in E$ is endowed with a length $l(e) \geq 0$ and a capacity $c(e) > 0$. For a path $P$ that connects $u$ and $v$, the {\it transmission time} of $P$ is defined as $t(P)=\mbox{\large$\Sigma$}_{e \in P} l(e) + \max_{e \in P}\!{(1 / c(e))}$. For a spanning tree $T$, let $P_{u,v}^T$ be the unique $u$--$v$ path in $T$. The {\sc quickest radius spanning tree problem} is to find a spanning tree $T$ of $G$ such that $\max _{v \in V} t(P^T_{root,v})$ is minimized. In this paper we present a 2-approximation algorithm for this problem, and show that unless $P =NP$, there is no approximation algorithm with a performance guarantee of $2 - \epsilon$ for any $\epsilon >0$. The {\sc quickest diameter spanning tree problem} is to find a spanning tree $T$ of $G$ such that $\max_{u,v \in V} t(P^T_{u,v})$ is minimized. We present a ${3 \over 2}$-approximation to this problem, and prove that unless $P=NP$ there is no approximation algorithm with a performance guarantee of ${3 \over 2}-\epsilon$ for any $\epsilon >0$.     

Approximation Algorithms for Quickest Spanning Tree Problems

Let $G=(V,E)$ be an undirected multigraph with a special vertex ${\it root} \in V$, and where each edge $e \in E$ is endowed with a length $l(e) \geq 0$ and a capacity $c(e) > 0$. For a path $P$ that connects $u$ and $v$, the {\it transmission time} of $P$ is defined as $t(P)=\mbox{\large$\Sigma$}_{e \in P} l(e) + \max_{e \in P}\!{(1 / c(e))}$. For a spanning tree $T$, let $P_{u,v}^T$ be the unique $u$--$v$ path in $T$. The {\sc quickest radius spanning tree problem} is to find a spanning tree $T$ of $G$ such that $\max _{v \in V} t(P^T_{root,v})$ is minimized. In this paper we present a 2-approximation algorithm for this problem, and show that unless $P =NP$, there is no approximation algorithm with a performance guarantee of $2 - \epsilon$ for any $\epsilon >0$. The {\sc quickest diameter spanning tree problem} is to find a spanning tree $T$ of $G$ such that $\max_{u,v \in V} t(P^T_{u,v})$ is minimized. We present a ${3 \over 2}$-approximation to this problem, and prove that unless $P=NP$ there is no approximation algorithm with a performance guarantee of ${3 \over 2}-\epsilon$ for any $\epsilon >0$.     

Some Relations between Approximation Problems and PCPs over the Real Numbers

In [10] it was recently shown that that is the existence of transparent long proofs for was established. The latter denotes the class of real number decision problems verifiable in polynomial time as introduced by Blum et al. [6]. The present paper is devoted to the question what impact a potential full real number theorem would have on approximation issues in the BSS model of computation. We study two natural optimization problems in the BSS model. The first, denoted by MAX-QPS, is related to polynomial systems; the other, MAX-q-CAP, deals with algebraic circuits. Our main results combine the PCP framework over with approximation issues for these two problems. We also give a negative approximation result for a variant of the MAX-QPS problem.