基于满意聚类的多模型建模方法

从系统输入输出数据出发, 首先在GK模糊聚类算法的基础上, 提出一种模糊满意聚类算法, 该算法能快速对系统进行用户满意的模糊划分 ;继而将其引入多模型建模过程中, 满意的系统划分数目即对应多模型个数, 然后针对不同的聚类建立起相应的子系统模型, 全局系统可视为各子模型的加权组合 ;最后通过几个典型实例验证了模糊满意聚类及基于此的多模型建模方法的有效性、准确性和快速性.     

启发式模糊聚类学习在函数逼近和非线性系统建模中的应用

在基于模糊G均值(FCM)聚类的模糊建模和神经模糊建模中,模糊聚类数是一个非常重要的参数,其决定了模型结构的复杂程度.本文提出了基于误差回溯的启发式模糊聚类学习方法.在建模过程中,该方法可以从较小的聚类数开始,根据误差检测,逐步填补输入聚类空间的"空洞",从而获得合适的模型规则数.函数逼近和非线性动态系统建模实验结果表明这种方法是简便而有效的.     

基于客观满意聚类的pH中和过程建模方法

针对一类生产中存在严重非线性的复杂工业过程——p H中和过程,基于客观聚类思想,并结合Gustafson-Kessel聚类,提出一种新的T-S模糊建模方法。根据用户对建模性能的满意度要求,通过迭代模糊聚类,进行模型前提结构和参数的辨识。仿真结果表明,与传统的模糊聚类等方法相比,该方法不依赖于系统的先验知识和预先定义的模糊隶属度函数,具有较为精简的结构和更好的逼近性能,对数据中的噪声具有一定的鲁棒性。     

基于混沌差分进化模糊聚类的多模型建模

针对模糊C-均值聚类算法存在对初始聚类中心敏感和聚类目标函数容易陷入局部最优的问题,提出了1种基于混沌差分进化模糊C-均值聚类的多模型建模方法.该方法采用混沌差分进化算法对模糊C-均值聚类的目标函数进行全局寻优,能有效的解决上述问题.将该方法应用于双酚A生产过程的质量指标软测量建模,仿真结果表明了该算法的有效性.     

基于在线聚类的模糊建模方法及其应用

针对复杂非线性动态系统的模糊建模问题,提出了一种基于在线聚类的模糊建模方法。该方法首先采用在线聚类算法辨识T-S模型的前提参数,然后采用递推最小二乘算法辨识结论参数。根据系统过程中新的数据信息,模糊规则可以自动增加、修改和删除,实现了模型结构和参数的在线辨识和更新。最后将提出的方法应用于Box-Jenkin煤气炉建模和二自由度机器人建模两个例子。仿真结果表明,基于该方法辨识的T-S模糊模型具有很高的精度,而且模型结构简单、建模速度快,便于工程应用。     

基于聚类和SVD算法的解释性模糊建模方法

提出了一种基于减法聚类算法构造解释性模糊模型的方法。首先指出模糊模型解释性的重要地位,分析影响解释性的主要因素;然后利用减法聚类算法辨识初始模糊模型,SVD算法和集合非冗余度约简初始模糊模型,从而提高其解释性;最后采用约束优化算法整体优化模型,提高其精度。PH值中和过程的模糊建模验证了该方法的有效性。     

基于特征加权模糊聚类的多模型软测量建模

针对化工生产过程中质量指标无法在线监测的问题,多模型软测量建模方法往往能取得不错的模型估计精度然而传统的模糊聚类算法都假定样本的各维特征对聚类的贡献相同,影响了聚类效果和模型估计精度.为了考虑样本各维特征对聚类的不同影响,提出一种新的特征加权模糊聚类算法.该算法在模糊聚类选代的基础上,逐步调整特征权值,最终有效改善了聚...     

基于自组织神经网络的非线性系统建模

针对基于T-S模糊模型的非线性系统建模问题,提出了一种基于自组织神经网络的新方法.在T-S模糊模型的建模中,目前常用的模糊C均值聚类算法存在迭代次数多,计算耗时的缺点.首先,利用竞争学习算法对输入空间进行聚类,基于此结果,借助于模糊C均值聚类算法进一步优化聚类结果,提取T-S模糊模型的规则前件隶属函数参数.然后,采用最小二乘法求得T-S模糊模型的规则后件参数,从而建立起非线性系统的T-S模糊模型.最后,仿真结果表明,该方法可以为模糊建模提供好的模型结构,并且有较高的计算效率和精度.     

基于增广输入变量的T-S 模糊模型建模

针对现有T-S 模糊模型建模精度与计算效率之间的矛盾, 提出一种利用增广输入变量进行T-S 模糊模型建模的方法. 对输入变量进行多项式增广处理后, 以核模糊?? 均值聚类算法配合聚类评价指标自适应获得最佳聚类数及相应的模糊划分, 并通过递推最小二乘计算得出T-S 模糊模型的后件参数. 提出可利用后件参数反推断前件结构的方法来快速有效地确定前件结构. 最后通过仿真验证了上述方法的有效性.

     

减法聚类-ANFIS在网络故障诊断的应用研究

提出了一种基于减法聚类-自适应模糊神经网络（ANFIS）的网络故障诊断建模方法。减法聚类算法生成初始模糊推理系统,ANFIS建立网络故障诊断原始模型,应用混合算法对模糊规则的参数进行训练并建立最终的模型。仿真实验表明基于减法聚类-ANFIS的建模方法是有效的;通过仿真结果比较,减法聚类-ANFIS的网络故障诊断能力及收敛速度均优于BP神经网络,更适合作为网络故障诊断模型。     

Development of a systematic methodology of fuzzy logic modeling

This paper proposes a systematic methodology of fuzzy logic modeling for complex system modeling. It has a unified parameterized reasoning formulation, an improved fuzzy clustering algorithm, and an efficient strategy of selecting significant system inputs and their membership functions. The reasoning mechanism introduces 4 parameters whose variation provides a continuous range of inference operation. As a result, we are no longer restricted to standard extremes in any step of reasoning. The fuzzy model itself can then adjust the reasoning process by optimizing the inference parameters based on input-output data. The fuzzy rules are generated through fuzzy c-means (FCM) clustering. Major bottlenecks are addressed and analytical solutions are suggested. We also address the classification process to extend the derived fuzzy partition to the entire output space. In order to select suitable input variables among a finite number of candidates (unlike traditional approaches) we suggest a new strategy through which dominant input parameters are assigned in one step and no iteration process is required. Furthermore, a clustering technique called fuzzy fine clustering is introduced to assign the input membership functions. In order to evaluate the proposed methodology, two examples-a nonlinear function and a gas furnace dynamic procedure-are investigated in detail. The significant improvement of the model is concluded compared to other fuzzy modeling approaches     

基于子集融合与规则简约的磨矿过程模糊建模

针对选矿厂磨矿生产过程的模糊建模问题,本文提出一种基于模糊集融合和规则简约的模糊建模方法.该方法针对基于数据建立的磨矿过程Takagi-Sugeno模型,采用模糊C均值聚类方法对同一变量下的隶属度函数参数进行聚类,得到对不同工况具有代表性的融合后的隶属度函数,来降低过度拟合的影响.此外,本文根据规则库中的规则权值,对前件相同的冗余规则进行约简,形成最终的离线模糊规则库,有效提高了规则库的泛化能力.为验证本文方法的有效性,分别采用经典数据与实际工业数据进行了实验论证,从精度和泛化能力上体现了本文方法的优势.     

On-line fuzzy modeling via clustering and support vector machines

In this paper, we propose a novel approach to identify unknown nonlinear systems with fuzzy rules and support vector machines. Our approach consists of four steps which are on-line clustering, structure identification, parameter identification and local model combination. The collected data are firstly clustered into several groups through an on-line clustering technique, then structure identification is performed on each group using support vector machines such that the fuzzy rules are automatically generated with the support vectors. Time-varying learning rates are applied to update the membership functions of the fuzzy rules. The modeling errors are proven to be robustly stable with bounded uncertainties by a Lyapunov method and an input-to-state stability technique. Comparisons with other related works are made through a real application of crude oil blending process. The results demonstrate that our approach has good accuracy, and this method is suitable for on-line fuzzy modeling.     

Traffic modeling, prediction, and congestion control for high-speednetworks: a fuzzy AR approach

In this study, a fuzzy autoregressive (fuzzy-AR) model is proposed to describe the traffic characteristics of high-speed networks. The fuzzy-AR model approximates a nonlinear time-variant process with a combination of several linear local AR processes using a fuzzy clustering method. We propose that the use of this fuzzy-AR model has greater potential for congestion control of packet network traffic. The parameter estimation problem in fuzzy-AR modeling is treated by a clustering algorithm developed from actual traffic data in high-speed networks. Based on the adaptive AR-prediction model and queueing theory, a simple congestion control scheme is proposed to provide an efficient traffic management for high-speed networks. Finally, using the actual Ethernet-LAN packet traffic data, several examples are given to demonstrate the validity of this proposed method for high-speed network traffic control     

Development of Quantum-Based Adaptive Neuro-Fuzzy Networks

In this study, we are concerned with a method for constructing quantum-based adaptive neuro-fuzzy networks (QANFNs) with a Takagi–Sugeno–Kang (TSK) fuzzy type based on the fuzzy granulation from a given input–output data set. For this purpose, we developed a systematic approach in producing automatic fuzzy rules based on fuzzy subtractive quantum clustering. This clustering technique is not only an extension of ideas inherent to scale-space and support-vector clustering but also represents an effective prototype that exhibits certain characteristics of the target system to be modeled from the fuzzy subtractive method. Furthermore, we developed linear-regression QANFN (LR-QANFN) as an incremental model to deal with localized nonlinearities of the system, so that all modeling discrepancies can be compensated. After adopting the construction of the linear regression as the first global model, we refined it through a series of local fuzzy if–then rules in order to capture the remaining localized characteristics. The experimental results revealed that the proposed QANFN and LR-QANFN yielded a better performance in comparison with radial basis function networks and the linguistic model obtained in previous literature for an automobile mile-per-gallon prediction, Boston Housing data, and a coagulant dosing process in a water purification plant.      

基于自适应模糊聚类的神经网络软测量建模方法

提出一种基于模糊聚类的神经网络软测量建模方法．该方法采用数据分组训练、自动确定模糊分类数、在线测量时分类中心自适应修正，降低了计算量，提高了建模精度．将该算法用于步进式加热炉钢坯温度预报的仿真结果表明，它能够解决钢坯温度难以在线测量的问题。     

一种基于遗传算法的模糊内模控制方法

针对一类非线性过程,提出了基于T-S模糊模型的非线性内模控制方法.使用遗传算法和模糊聚类方法进行模糊建模,解决了非线性内模控制方法中建立精确的模型及其逆模型困难的问题.通过模糊辨识获得过程的T-S模型及逆模型,并以此设计了内模控制器.最后,将该方法应用于一类非线性过程的控制,仿真结果表明该方法的有效性.     

Modeling and advanced control method of PVC polymerization process

Based on data driven modeling theory, PVC polymerization process modeling and intelligent optimization control algorithm is studied. Firstly, a multi-T–S fuzzy neural networks soft-sensing model combining the principal component analysis (PCA) and fuzzy c-means (FCM) clustering algorithm is proposed to predict the convention rate and velocity of Vinyle Chloride Monomer (VCM). The proposed hybrid learning algorithm utilizing the harmony search (HS) and least square method is used to adjust the model premise parameters and consequent parameters. Secondly, the generalized predictive control (GPC) algorithm of polymerizer temperature based on segmental affine is proposed. According to dynamic equation of polymerizer temperature deduced by heat balance mechanism, the segmental affine model is built by temperature and convention rate of the polymerizer. Then linear matrix inequality (LMI) method is used to design the controller. Finally, simulation results and industrial application show the validity and feasibility of the proposed control strategy.     

Rotary drying process modeling and online compensation

Rotary drying process modeling is a complex procedure due to the difficulties in measurement and estimation of kinetic model parameters. To solve the problem, a hybrid modeling method with online compensation is proposed in the present study. A mathematical model is built to describe the axial characteristics of rotary drying process. The drying rate which is the key parameter in the model is estimated by using a SVR-based fuzzy modeling approach, which can automatically extract fuzzy IF-THEN rules from support vectors. Laboratory experiments are conducted to obtain the drying rate sample data for the modeling purpose. In order to reduce the modeling errors for an industrial rotary dryer and improve the hybrid model prediction accuracy, an online matching coefficient is introduced, and a method based on improved online SVR is then applied for modeling error compensation. The experiment dada based modeling results have verified the effectiveness and demonstrated the accuracy and adaptability of the proposed hybrid modeling method.     

一种基于数据挖掘的模糊建模方法

为提高非线性系统模糊建模的速度和精确度,提出一种快速有效的基于数据挖掘的非线性系统模糊建模方法.该方法先采用改进的减法聚类结合模糊C-均值聚类进行结构辨识,在解决初始化问题的同时减少计算量,进而提高建模速度;然后利用带动态遗忘因子的递推最小二乘法进行后件参数辨识,减小动态误差,提高建模精度.将提出的方法应用于Box-J...