基于感知推理方法的语言动力系统性质及数据驱动设计研究(英文)

The linguistic dynamic systems(LDSs) based on type-1 fuzzy sets can provide a powerful tool for modeling, analysis,evaluation and control of complex systems. However, as pointed out in earlier studies, it is much more reasonable to take type-2fuzzy sets to model the existing uncertainties of linguistic words. In this paper, the LDS based on type-2 fuzzy sets is studied, and its reasoning process is realized through the perceptual reasoning method. The properties of the perceptual reasoning method based LDS(PR-LDS) are explored. These properties demonstrated that the output of PR-LDS is intuitive and the computation complexity can be reduced when the consequent type-2 fuzzy numbers in the rule base satisfy some conditions. Further, a data driven method for the design of the PR-LDS is provided. At last, the effectiveness and rationality of the proposed data-driven method are verified by an example.     

Data-driven fuzzy clustering based on maximum entropy principle and PSO

To identify the optimum fuzzy rule base is the major difficulty in designing fuzzy model. To design optimum fuzzy rule base, which is traditionally achieved by tedious trial and error process, from numerical data, a novel data-driven fuzzy clustering method based on maximum entropy principle (MEP) and particle swarm optimization (PSO) is proposed. In this algorithm, the memberships of output variables are inferred by maximum entropy principle, and the centers of fuzzy rule base are optimized by PSO. Comparing with the method that designing fuzzy rule base only by PSO or other evolutionary computation methods, the number of parameters to be optimized decreased greatly, and the computation cost declined. To check the effectiveness of the suggested approach, three examples for modeling are examined comparing with the method only using PSO. The performance of the identified fuzzy models is demonstrated.     

一种基于核集与相似性的模糊推理方法

在稀疏规则库条件下,当给定的输入落入规则"间隙"时,采用传统的模糊推理方法是得不到任何结论的.学者已经证明模糊推理本质上就是插值器.Koczy和Hirota首先提出了KH线性插值推理方法,然而推理结果存在着无法保证凸性和正规性等问题.为了能有一个较好的插值推理结果,本文提出了一种基于核集与相似性的模糊插值推理方法,并把此方法扩展到多维变量的情况,该方法不仅推理简单,推理结果较好,并且能很好地保证推理结果的凸性和正规性.这为智能系统中的模糊推理提供了一个非常有用的工具.     

New approaches to H∞ controller designs based on fuzzy observers for T-S fuzzy systems via LMI

The problems of relaxed quadratic stability conditions, fuzzy observer designs and H_∞ controller designs for T-S fuzzy systems have been studied. First new stability conditions are obtained by relaxing the stability conditions derived in previous papers. Secondly, new fuzzy observers based on the relaxed stability conditions for the T-S fuzzy systems have been proposed. Thirdly two sufficient LMI conditions, which guarantee the existence of the H_∞ controllers based on fuzzy observers for the T-S fuzzy systems have been proposed. The conditions are not only simple, but also consider the interactions among the fuzzy subsystems. Finally by some examples, using the LMI technique, we show that the regulators, the fuzzy observers and the H_∞ controller designs based on new observers for the T-S fuzzy systems are very practical and efficient.     

规则不完备的模糊系统新规则在线生成

由专家经验和输入输出样本数据得到的模糊规则常常是不完备的,规则不完备的模糊系统因为缺少一些规则,对于某些可能的输入值产生的输出会很不合理。文章提出了用插值法在必要时在线生成新规则,并设计了一个模糊系统来插值填充规则库空格。实验结果表明,这种方法大大增强了规则不完备的模糊系统在整个输入域上输出的连续性和稳定性。     

MULTITUNING FUZZY CONTROL SYSTEMS DESIGN

A multituning fuzzy control system structure that involves two simple, but effective tuning mechanisms, is proposed: one is called fuzzy control rule tuning mechanism (FCRTM); the other is called dynamic scalar tuning mechanism (DSTM). In FCRTM, it is used to generate the necessary control rules with a center extension method. In DSTM, it contains three fuzzy IF-THEN rules for determining the appropriate scaling factors for the fuzzy control system. In this paper, a method based on the genetic algorithm (GA) is proposed to simultaneously choose the appropriate parameters in FCRTM and DSTM. That is, the proposed GA-based method can automatically generate the required rule base of fuzzy controller and efficiently determine the appropriate map for building the dynamic scalars of fuzzy controller. A multiobjective fitness function is proposed to determine an appropriate parameter set such that not only the selected fuzzy control structure has fewer fuzzy rules, but also the controlled system has a good control performance. Finally, an inverted pendulum control problem is given to illustrate the effectiveness of the proposed control scheme.     

Adaptive fuzzy controller for non-affine systems with zero dynamics

In this article, the direct adaptive fuzzy control problem is investigated for a class of general non-linear systems with zero dynamics. The direct adaptive fuzzy controller is developed based on a unified observer which is used to estimate the time derivatives of the output. The corrective term of the proposed observer involves a well-defined design function which is shown to be satisfied by the commonly used high-gain-based observers, namely for the usual high-gain observers and the sliding-mode observers together with their implementable versions. By using a general error function, and without resorting to the famous strictly positive real condition or the filtering of the observation error, a general proportional–integral (PI) law for updating the fuzzy parameters is proposed. Ultimately boundedness of the error signals is shown through Lyapunov's direct method. Theoretical results are illustrated through two simulation examples.     

Real time traffic delay optimization using shadowed type-2 fuzzy rule base

Traffic networks are getting big and complex day by day with a rapid traffic growth. Existing Type-2 (T2) fuzzy logic works well in optimizing the waiting time of traffic at a big junction, but the rule base of T2 fuzzy logic is heavily dependent on previous traffic data, rather than real-time data. Moreover, it fails in changing and updating the waiting time in any junction with a high rate of traffic. In addition, very big junctions contain dynamic traffic data that is characterized by a high level of uncertainty, which is difficult to be handled by type-2 fuzzy logic. To cope with this situation, Shadowed Type-2 (ST2) fuzzy logic is proposed as it works well in the domain having very clumsy and uncertain data. It increases the uncertainty of a fuzzy set by partitioning it into different region. Thus, based on ST2 fuzzy rule base, a ST2 fuzzy waiting time simulator is created, whose output is implemented in a proposed real-time traffic-based Time Optimized Shortest Path (TOSP) model. It helps in structuring the optimized time path from one location to another. This can be done by taking real time traffic data from the upcoming junction, calculating the waiting time using ST2 fuzzy rule base, and finally directing the vehicle to take its optimized path, which results in a reduction in the overall waiting time of each junction. To demonstrate the superiority of the proposed model, a case study of a multi-directional (six directional) junction is presented. Success of this model easies the process of proposing it as a mobile application, which can help in reducing the waiting time in junctions of metropolitan areas.     

一种模糊相似关系的基因表达数据聚类方法

对于时间序列的基因表达数据,传统的聚类算法都是以距离为相似性度量标准,没有考虑基因随时间变化的相似趋势。从基因变化的趋势出发,构造了一种新的模糊相似关系矩阵,提出了改进的基于模糊相似关系的聚类算法,并以该算法计算FCM的初始聚类中心。将该方法应用在酵母菌基因表达数据中,实验结果表明该算法不仅克服了FCM算法易陷入局部极小值、对初值敏感的缺点,而且能够发现一些表达模式变化趋势相似的共调控基因。     

Automatic generation of a fuzzy rule base for constant turning force

An automatic fuzzy rule base generation method is proposed to control nonlinear and timevarying turning processes with constant cutting forces. Based on this study, the optimum fuzzy rule base for the control of turning processes can be self-organized without the need for experienced manufacturing engineers. A fuzzy logic controller based on these fuzzy rules can adjust feed rate on line to achieve an optimal production rate in turning operations.     

Pattern discovery of fuzzy time series for financial prediction

A fuzzy time series data representation method based on the Japanese candlestick theory is proposed and used in assisting financial prediction. The Japanese candlestick theory is an empirical model of investment decision. The theory assumes that the candlestick patterns reflect the psychology of the market, and the investors can make their investment decision based on the identified candlestick patterns. We model the imprecise and vague candlestick patterns with fuzzy linguistic variables and transfer the financial time series data to fuzzy candlestick patterns for pattern recognition. A fuzzy candlestick pattern can bridge the gap between the investors and the system designer because it is visual, computable, and modifiable. The investors are not only able to understand the prediction process, but also to improve the efficiency of prediction results. The proposed approach is applied to financial time series forecasting problem for demonstration. By the prototype system which has been established, the investment expertise can be stored in the knowledge base, and the fuzzy candlestick pattern can also be identified automatically from a large amount of the financial trading data.     

SIGNIFICANT FUZZY RULES EXTRACTION BY AN SVD–QR-BASED APPROACH

ABSTRACT

In this article, an SVD–QR-based approach is proposed to extract the important fuzzy rules from a rule base with several fuzzy rule tables to design an appropriate fuzzy system directly from some input-output data of the identified system. A fuzzy system with fuzzy rule tables is defined to approach the input-output pairs of an identified system. In the rule base of the defined fuzzy system, each fuzzy rule table corresponds to a partition of an input space. In order to extract the important fuzzy rules from the rule base of the defined fuzzy system, a firing strength matrix determined by the membership functions of the premise fuzzy sets is constructed. According to the firing strength matrix, the number of important fuzzy rules is determined by the Singular Value Decomposition SVD, and the important fuzzy rules are selected by the SVD–QR-based method. Consequently, a reconstructed fuzzy rule base composed of significant fuzzy rules is determined by the firing strength matrix. Furthermore, the recursive least-squares method is applied to determine the consequent part of the reconstructed fuzzy system according to the gathered input-output data so that a fine fuzzy system is determined by the proposed method. Finally, three nonlinear systems illustrate the efficiency of the proposed method.     

Multichannel image contrast enhancement based on linguistic rule-based intensificators

This study follows the direct approach to image contrast enhancement, which changes the image contrast at each its pixel and is more effective than the indirect approach that deals with image histograms. However, there are only few studies following the direct approach because, by its nature, it is very complex. Additionally, it is difficult to develop an effective method since it is required to keep a balance in maintaining local and global image features while changing the contrast at each individual pixel. Moreover, raw images obtained from many sources randomly influenced by many external factors can be considered as fuzzy uncertain data. In this context, we propose a novel method to apply and immediately handle expert fuzzy linguistic knowledge of image contrast enhancement to simulate human capability in using natural language. The formalism developed in the study is based on hedge algebras considered as a theory, which can immediately handle linguistic words of variables. This allows the proposed method to produce an image contrast intensificator from a given expert linguistic rule base. A technique to preserve global as well as local image features is proposed based on a fuzzy clustering method, which is applied for the first time in this field to reveal region image features of raw images. The projections of the obtained clusters on each channel are suitably aggregated to produce a new channel image considered as input of the pixelwise defined operators proposed in this study. Many experiments are performed to demonstrate the effect of the proposed method versus the counterparts considered.     

飞机座舱压力专家模糊预控方法仿真研究

针对新型战机高空高速俯冲及俯冲转平飞情况下带来的座舱压力控制难题,提出一种飞机座舱压力专家模糊PID预控方法。在飞机高速俯冲时,基于压力调节系统时间延迟及飞机高度变化率改进常规模糊PID控制策略并提高座舱压力调整速度;在飞机状态转换时,利用专家控制器根据知识库及状态转换时间预测调整模糊PID控制策略,并引入重置机制以改善调整性能。经过知识库的动态学习,得出飞机状态转换时,采用模糊PID控制、模糊预控、重置PID控制参数的专家控制策略具有最佳的控制效果的结论。通过仿真实验验证了该方法的有效性。     

一种面向大数据的快速自动聚类算法

针对大数据环境下高维数据聚类速度慢、准确率低的问题,提出了一种面向大数据的快速自动聚类算法(FACABD)。FACABD聚类算法利用谱聚类算法对大数据集进行归一化和列降维,提出了一种新的快速区域进化的粒子群算法(FRE-PSO),并利用该算法进行行降维;然后在降维处理后的数据基础上,引入聚类模糊隶属度基数,自动发现簇的数目,根据类簇数目,采用FRE-PSO算法结合模糊聚类算法快速完成自动聚类。在人工生成数据集和UCI机器学习数据集上的实验结果表明,该算法能够在数据驱动下快速自动聚类,有效地提高了运行速度和精度。     

基于观测器的不确定T-S模糊系统鲁棒镇定

为带有参数不确定性的T-S模糊控制系统提出了新的基于观测器的鲁棒输出镇定条件. 该条件用来设计模糊控制器和模糊观测器. 为了设计模糊控制器和模糊观测器, 用T-S模糊模型来表示非线性系统, 并运用平行分布补偿观念. 充分条件基于二次Lyapunov函数, 通过将模糊系统的鲁棒镇定条件表述为一系列矩阵不等式, 比以往文献中列出的条件具有更小的保守性. 该不等式为双线性矩阵不等式, 可分两步骤先后解得使T-S模糊系统镇定的控制器增益和观测器增益. 最后, 通过对一个具有不确定性的连续时间非线性系统控制的例子证明了提出方法比以往方法更宽松.     

风力发电系统传感器故障诊断

针对非线性风力发电系统,提出了一种基于滑模观测器的传感器故障诊断方法.基于考虑传感器加性故障的非线性动态模型,利用T--S模糊理论建立风力发电系统全局T--S模型,设计模糊T--S系统滑模故障观测器,产生对故障具有敏感性的残差,实现故障检测.通过等价输出控制方法来维持滑模运动,直接获取故障信息,重构传感器故障.最后以三叶片水平轴风力发电系统为例,仿真验证了该方法的有效性与可靠性.     

Fuzzy observers for anaerobic WWTP: Development and implementation

In this paper a set of fuzzy observers for anaerobic digestion is proposed to estimate variables difficult to measure in a completely stirred tank reactor. First, a mathematical model for the process is stated and experimentally validated. Then, a methodology based on principal components analysis is developed to select fuzzy variables, which allow the local observers to be adequately activated. The active local observers are interpolated using the Takagi–Sugeno approach, in order to recover the non-linear behaviour; to ensure their adequate performance, the respective stability analysis is included. The whole estimation scheme is validated via simulations and tested in a real process.     

基于冲突分析的犹豫模糊多属性决策

在基于犹豫模糊距离的多属性决策问题中,定义距离测度和计算属性权重是两个关键点.在定义犹豫模糊数的距离测度时,针对添加相同元素,补齐较短犹豫模糊数,改变原有含义的问题,文中提出同时扩展两个犹豫模糊数使其达到相同长度的方法.再根据Pawlak的冲突分析理论,建立犹豫模糊信息系统的冲突分析模型,提出基于冲突度的计算属性权重的方法.最后给出求解犹豫模糊信息系统下多属性决策问题的具体方法.企业发展规划的实例说明文中方法的有效性和可行性.