Multivariable Fuzzy Predictive Control Based on the Modified CPN Model

Through modifying the CPN model, a kind of multivariable fuzzy model is put forward, and the matching fuzzy multistep predictive control algorithm is deduced based on the model. The modified model works in a competitive output manner which results in its local representation property. While studying on line, only a few parameters need to be regulated. So the model has the merits of fast learning and on line self organizing modeling. The control algorithm is simple, adaptive and useful in multivariable and time delay systems. Applying the algorithm in a paper making system, simulation shows its good effect.

Multivariable fuzzy control of residential inverter-driven air conditionings

A model free intelligent muhivariable fuzzy controller （MFC） designed for modulating the vapor compression cycles in a residential inverter-driven air conditioning is proposed. The novel controller combines a traditional fuzzy controller （TFC） and an additional coupling fuzzy controller, the coupling fuzzy controller is introduced to compensate for the unknown cross-coupling effects of this muhivariable system. In order to evaluate the control performance of the MFC, it is digitally implemented in terms of regulating the desired evaporating temperature and superheat. The experimental results show the effectiveness of the MFC for improvement of system performance and energy efficiency.

Fuzzy Geometric Programming in Multivariate Stratified Sample Surveys in Presence of Non-Response with Quadratic Cost Function

In this paper, the problem of non-response with significant travel costs in multivariate stratified sample surveys has been formulated of as a Multi-Objective Geometric Programming Problem (MOGPP). The fuzzy programming approach has been described for solving the formulated MOGPP. The formulated MOGPP has been solved with the help of LINGO Software and the dual solution is obtained. The optimum allocations of sample sizes of respondents and non respondents are obtained with the help of dual solutions and primal-dual relationship theorem. A numerical example is given to illustrate the procedure.

基于大数据的水肥一体机控制应用研究

针对传统灌溉方式水资源利用率低、农业用水浪费严重、肥料过度使用造成资源浪费和水污染等问题,基于大数据对水肥一体机的控制进行了设计。为了保证水肥一体机的灌溉精准性以及获取数据的精确性,采用自适用加权融合算法对获取的数据进行处理;为了对水肥浓度进行智能控制,采用PID控制器和自适应模糊控制器结合的方式对肥液浓度进行控制;为了验证该水肥一体机的性能,对其进行了浓度试验和肥料配比控制试验。结果表明:肥液浓度可以通过电导率仪进行测量,且设计控制器对肥液浓度的控制效果更优。

多元Fuzzy线性回归

在处理某些系统模型中,有些输出或输入量的值是通过人的心理测量给出的。这时我们可认为此系统具有Fuzzy结构。这种Fuzzy结构一般可用带有Fuzzy参数的Fuzzy线性函数表示,而Fuzzy线性函数可由Zadeh的扩展原理得到。这样用Fuzzy线性函数来描述系统的Fuzzy结构便形成了Fuzzy线性回归分析。当输出输入量均是多个时便是多元Fuzzy线性回归。在经典的多元回归中,观测向量值与估计向量值之间的差异一般认为是测量误差,本文处理的多元Fuzzy回归中,我们认为其差异是由于系统的Fuzzy性造成的而反映在回归方程的Fuzzy参数上,这种Fuzzy参数一般表示一种可能性分布。文章讨论了多元Fuzzy回归模型的拟合问题,给出了其Fuzzy向量参数的估计方法,并将其计算方法归结为求解某线性规划问题。本文使用的Fuzzy向量限制在三角族内,而对其它形式的常用Fuzzy向量也没有实质困难。

基于多元状态估计及模糊综合评价分析的汽轮机故障预警

汽轮机的故障模式、故障特征具有多样性。当前的故障预警方法大多针对单一故障模式,无法用于全面评估汽轮机整体的健康状态。为此,将模糊综合评价与多元状态估计结合,构建了更加全面的汽轮机故障预警模型。首先,基于汽轮机的典型故障模式构建故障特征集;然后,通过多元状态估计算法获得估计向量,并用滑动窗口法对结果进行平滑处理,计算得到汽轮机各个故障模式的实时偏离度;最后,基于模糊综合评价综合分析多种故障的偏离程度以确定汽轮机整体的健康状态。使用某电厂汽轮机的故障数据进行模拟仿真验证,结果表明利用该方法能够提前捕捉到汽轮机的异常状态、实现故障的预警。

模糊物元分析方法在潜山储层有效性评价中的应用

海洋勘探中潜山油气藏勘探技术的提高和突破,对录井在储层有效性评价方面提出更高标准的需求——潜山储层解释评价精细化和定量化。潜山储层的地质录井解释评价也面临数据多样化而使定性评价难度增大的问题,目前储层可靠性评估手段无法实现将多个地质录井数据降维,采用模糊物元分析法对气测录井、三维定量荧光录井、元素录井、衍射录井等多参数进行融合评价,可将多维降至一维,综合评价储层的物性和含油气性;依据工程录井参数的机械比能、机械比能基值引入孔缝指数,用其表征潜山储层的物性;最终通过模糊物元的综合评价指标和孔缝指数交会,将储层有效性的标准定量化,给潜山储层有效性评价提供一种新的方法。将该评价方法在某区块新钻井进行应用,取得了较好的应用效果。

Event-triggered fuzzy neural multivariable control for a municipal solid waste incineration process

Because of coupling,nonlinearity,and uncertainty in a municipal solid waste incineration(MSWI)process,a suitable multivariable controller is difficult to establish under strong disturbance.Additionally,the problems of reducing mechanical wear and energy consumption in the control process should also be considered.To solve these problems,an event-triggered fuzzy neural multivariable controller is proposed in this paper.First,the MSWI object model based on the multiinput multioutput TakagiSugeno fuzzy neural network is established using a data-driven method.Second,a fuzzy neural multivariable controller is designed to control the furnace temperature and flue gas oxygen content synchronously under external disturbance.Third,an event-triggered mechanism is constructed to update the control rate online while ensuring control effects.Then,the stability of the proposed control strategy is proven through the LyapunovⅡtheorem to guide its practical application.Finally,the effectiveness of the controller is verified using the real industrial data of an MSWI factory in Beijing,China.The experimental results show that the proposed control strategy greatly improves the control efficiency,reduces energy consumption by 66.23%,and improves the multivariable tracking control accuracy.

长江上海段异常船舶识别研究

针对长江上海段的船舶异常问题,提出了一种基于多元大数据技术的判断模型。现有异常船舶识别方法存在数据孤岛难题,难以发现有效关联关系,基于此,研究长江上海段异常船舶识别方法。首先,基于多元大数据技术整合船舶航迹信息、人船关联信息等多个数据源,并利用粒子群优化模糊神经网络实现异常船舶识别。实验结果表明,设计方法的识别精度最高达到了93%,具有一定的实用性。

FINGERPRINT-BASED KEY BINDING/RECOVERING SCHEME BASED ON FUZZY VAULT

This letter proposes fingerprint-based key binding/recovering with fuzzy vault. Fingerprint minutiae data and the cryptographic key are merged together by a multivariable linear function. First, the minutiae data are bound by a set of random data through the linear function. The number of the function’s variables is determined by the required number of matched minutiae. Then, a new key de- rived from the random data is used to encrypt the cryptographic key. Lastly, the binding data are protected using fuzzy vault scheme. The proposed scheme provides the system with the flexibility to use changeable number of minutiae to bind/recover the protected key and a unified method regardless of the length of the key.

On-Line Real Time Realization and Application of Adaptive Fuzzy Inference Neural Network

In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.

Compromise Allocation for Combined Ratio Estimates of Population Means of a Multivariate Stratified Population Using Double Sampling in Presence of Non-Response

This paper is an attempt to work out a compromise allocation to construct combined ratio estimates under multivariate double sampling design in presence of non-response when the population mean of the auxiliary variable is unknown. The problem has been formulated as a multi-objective integer non-linear programming problem. Two solution procedures are developed using goal programming and fuzzy programming techniques. A numerical example is also worked out to illustrate the computational details. A comparison of the two methods is also carried out.

基于多元区域集划分的工业数据流概念漂移检测

为了快速适应非平稳环境中工业数据流的分布变化,需要在非结构化和噪声干扰的数据中准确、实时的完成概念漂移的检测.本文提出了一种基于多元区域集划分的工业数据流概念漂移检测算法(Concept Drift detection-Multivariate region set Partition,CDMP).首先基于实例模糊密度进行多元区域集划分,根据划分的若干模糊分区集合,识别概念漂移发生的区域.概念漂移的持续发生会显著降低基于多元区域集构建的模型的分类性能,CDMP通过构建多元历史模型池来保留具有多样性的历史模型,以降低模型调整或再训练造成的性能损耗,同时保证概念漂移检测中准确性.CDMP在不同数据集上进行了性能测试.实验结果表明,CDMP实现了对历史模型多样性的保留和重用,能够在不同噪声水平的工业物联网环境中实现对重现型、突发型等多类型概念漂移的准确检测.

改进粒子群优化的中药提取参数模糊PID解耦控制研究

为解决中药提取过程中温度和压力控制存在的非线性、大惯性和多变量耦合等问题,建立了一种基于改进粒子群优化的模糊PID多变量解耦控制模型。在中药提取过程的动态数学模型基础上,结合多变量前置反馈解耦器对模型进行解耦,降低温度和压力间的耦合度;引入线性递减和目标值自适应融合的动态惯性权重策略,用改进粒子群优化算法对模糊PID控制器的比例因子和量化因子进行优化,避免参数陷入局部最优;最后,在Simulink中对所设计的控制模型进行仿真。结果表明:改进算法优化后的模糊PID解耦控制器的曲线超调接近于0,温度调节时间缩短97%,压力调节时间缩短55%,系统无振荡,基本消除了稳态误差,提高了中药提取过程中温度和压力的控制精度。

污水处理过程多变量自适应变论域模糊控制

由于污水处理过程具有非线性、时变性和滞后性的特点,传统控制方法难以取得满意的控制效果。为了实现精确跟踪控制,提出了一种基于自适应变论域模糊控制(adaptive variable universe fuzzy control,AVUFC)的污水处理过程多变量控制方法,实现了溶解氧和硝态氮的底层回路控制。在AVUFC中,根据性能指标设计了基于自适应差分进化算法的伸缩因子调整策略,在线自适应调节模糊控制论域,降低工况变化对控制器性能的影响,提高控制器的动态性能和稳态精度。利用国际基准仿真平台BSM1进行实验验证。仿真结果表明,当入水流量和组分浓度发生剧烈变化时,所提出的自适应变论域模糊控制策略能够通过在线优化模糊规则以快速适应复杂工况;与PID控制、模糊逻辑控制、定式变论域模糊控制等方法相比,自适应变论域模糊控制在平稳性、抗干扰性和瞬态响应能力方面更具优势。

基于集成精细复合多元多尺度模糊熵的齿轮箱故障诊断

针对齿轮箱故障信号具有非线性和非平稳性的特点,且目前的方法对其特征提取不够充分这一问题,对不同形式粗粒化方法的集成、多通道信号处理方法在模糊熵算法上的应用进行了研究,提出了一种新的特征提取方法,即集成精细复合多元多尺度模糊熵(ERCmvMFE)算法,在此基础上,结合t分布随机邻域嵌入(t-SNE)和人工鱼群算法优化的核极限学习机(AFSA-KELM),提出了一种新的齿轮箱故障综合诊断方法。首先,采用多种形式粗粒化方法的集成方法以及多通道信号处理方法,对模糊熵算法进行了改进,并进行了齿轮箱故障的初始特征提取;然后,通过t-SNE压缩原始故障特征,实现了维数的约简,并将低维故障特征输入至AFSA-KELM中进行了故障的分类识别;最后,为了对ERCmvMFE方法的特征提取性能进行测试,采用QPZZ-II旋转机械故障模拟测试平台进行了相关的实验。实验结果表明:采用新的齿轮箱故障综合诊断方法能够对不同类型的齿轮箱故障进行可靠诊断,对齿轮箱5种工况下的20次识别实验中,获得的平均准确率可达98.92%,标准差为0.956,识别准确率和稳定性均优于其他对比方法。研究结果表明:采用ERCmvMFE算法能够更充分地提取出齿轮箱的故障特征,因此,基于该特征提取方法的故障诊断方法具有更高的齿轮箱故障识别准确率。

重介工艺悬浮液密度和液位的多变量模糊控制方法研究

研究了重介选煤工艺介质密度和液位系统的动态特性,液位和密度的多变量模糊控制方法.该方法采用2个独立的模糊控制器,分别控制液位和密度.模拟结果表明这种控制方法规则设计简单、控制效果好.该方法在主再洗工艺的低密度介质循环系统控制中进行了试验,结果表明能满足重介选煤介质循环系统的控制要求.

基于多变量多尺度模糊熵的行星齿轮箱故障诊断

行星齿轮箱被广泛用于风电、直升机和工程机械等大型复杂机械装备中。当行星齿轮箱发生故障时,振动信号往往表现出非线性和非平稳特征,多尺度熵理论能够有效地衡量振动信号的非线性和复杂性变化。同时为了尽可能地利用多通道振动信号信息来提高故障诊断的效率,将评价同步多通道数据多变量复杂度的多变量多尺度熵理论引入到行星齿轮箱故障诊断。针对其统计特征稳定性差的问题,提出了多变量多尺度模糊熵,并在基础上,提出了一种新的行星齿轮箱故障诊断方法。将提出的方法应用于行星齿轮箱故障试验数据分析,并与现有方法进行对比,结果表明了所提方法的有效性和优越性。

200 MW及以下容量机组除氧器和凝汽器水位多变量模糊控制系统

对200 MW及以下容量机组除氧器和凝汽器水位被控对象进行分析，结合扬州发电有限公司4号机组的实际工程改造，提出一种适合于国内200 MW及以下容量机组除氧器水位和凝汽器水位的多变量模糊控制方法。实际运行表明，该控制方法具有优良的控制品质，确保了该控制系统长期稳定投入。

多变量系统的软测量建模研究

研究加氢裂化分馏塔多个产品的质量指标同时预报的一类 MIMO软测量建模问题 ,采用 RBF网络和 Fuzzy ARTMAP网络对该 MIMO系统进行建模 ,并用 RBF网络建立了每个输出的独立的MISO软测量模型。应用实例表明 。