基于正交最小二乘估计的非线性系统模糊辨识

提出了一种基于正交最小二乘的模糊模型结构和参数辨识方法．首先，基于正交最小二乘方法分析模糊模型的模糊关系矩阵．通过分析正交向量在模型中贡献的大小，确定模糊模型的结构，即确定模糊模型的规则数、规则．另外，再次通过正交最小二乘方法确定模糊模型的结论参数，实现模糊模型结构和参数的优化．为了证明该方法的有效性，采用该文方法对Box-Jenkins煤气炉数据系统进行建模研究，仿真结果表明该文方法能够对非线性系统进行辨识．     

基于模糊模型的结构和参数的一体化辨识

基于模糊集合的模糊建模捕述复杂、病态、非线性系统的特性是一种有效方法．文中讨论了从样本数据中通过正交变换和模糊聚类获取模糊规则的方法．利用正交最小二乘对模糊聚类的结果进行变换，采用CGS（Classical Gram—Schmidt）方法确定对建模贡献大的规则，删除对建模贡献小的规则，并对模型中的参数进行估计，能够同时模对糊模型的结构和参数进行辨识．仿真结果表明，提出的方法能够对非线性系统进行模糊建模．     

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。     

基于模糊分类的模糊神经网络辨识方法及应用

基于改进的T-S模型，提出一种自适应模糊神经网络模型(AFNN)，给出了网络的连接结构和学习算法。基于竞争学习算法的模糊分类器确定系统的模糊空间和模糊规则数，并得出每个样本对每条规则的适用程度。利用卡尔曼滤波算法在线辨识删的后件参数。AFNN结构简洁，逼近能力强，能够显著提高辨识精度，并且在线辨识的模糊模型简单有效。将该AFNN用于非线性系统的模糊辨识和化工过程连续搅拌反应器(CSTR)的建模中，仿真结果验证了该方法的有效性，表明该网络能够实现复杂非线性系统的建模，而且建模精度高、收敛速度快。可当作复杂系统建模的一种有效手段。     

基于模糊神经网络的非线性系统模型的辨识

该文提出一种非线性系统的模型辨识方法．利用关系聚类法来进行结构辨识，从而自动获得模糊规则库，并可以得到模糊系统的初始参数，在聚类的基础上，构造一个与之相匹配的模糊神经网络，用它的学习算法来训练网络，得到一个精确的模糊模型，从而实现参数辨识，通过对两个非线性系统辨识的仿真结果验证了该方法的有效性。     

蚁群聚类算法的T-S模糊模型辨识

基于T-S模型,提出一种非线性系统的模型辨识方法。利用蚁群聚类算法来进行结构辨识,确定系统的模糊空间和模糊规则数。在聚类的基础上,利用遗传算法辨识模糊模型的后件加权参数,得到一个精确的模糊模型,从而实现参数辨识。仿真结果验证了该方法的有效性,表明该方法能够实现非线性系统的辨识,辨识精度高,可当作复杂系统建模的一种有效手段。     

基于改进模糊神经网络的软测量建模方法

提出了一种改进的模糊神经网络软测量建模方法，采用规则化的平均输出隶属度函数作为模糊基函数进行反模糊化运算；在训练网络时,部分参数采用Levenberg-Marquardt算法来训练，另一部分采用一阶梯度下降法．最后用该建模方法建立了聚合反应中熔融指数的软测量模型，并与一般的模糊神经网络软测量模型进行比较．结果表明改进的模糊神经网络对初始值的选择不敏感,具有很好的收敛性，同时还能达到指定的预测精度，很适合工程应用．     

基于T-S模型的锌钡白干燥煅烧过程自适应神经模糊推理系统建模

针对锌钡白干燥煅烧过程建模难的问题，提出了一种基于T-S模型的自适应神经模糊推理系统(ANFIS)建模方法．通过对模糊辨识系统的结构辨识和参数辨识，使网络自主、迅速地收敛到要求的输入输出关系．文章讨论了该网络的结构和学习算法，并通过仿真研究得出其良好的实际应用价值．     

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...     

基于蚁群聚类算法的非线性系统辨识

基于T-S模型提出一种非线性系统的模型辨识方法.利用蚁群聚类算法进行结构辨识,确定系统的模糊空间和模糊规则数.在聚类的基础上,利用遗传算法辨识模糊模型的后件加权参数,得到一个精确的模糊模型,从而实现了参数辨识.仿真结果验证了所提出方法的有效性,表明该方法能够实现非线性系统的辨识,而且辨识精度较高.     

A parameter optimization method for radial basis function type models

This paper considers the nonlinear systems modeling problem for control. A structured nonlinear parameter optimization method (SNPOM) adapted to radial basis function (RBF) networks and an RBF network-style coefficients autoregressive model with exogenous variable model parameter estimation is presented. This is an off-line nonlinear model parameter optimization method, depending partly on the Levenberg-Marquardt method for nonlinear parameter optimization and partly on the least-squares method using singular value decomposition for linear parameter estimation. When compared with some other algorithms, the SNPOM accelerates the computational convergence of the parameter optimization search process of RBF-type models. The usefulness of this approach is illustrated by means of several examples.     

Developing soft sensors using hybrid soft computing methodology: a neurofuzzy system based on rough set theory and genetic algorithms

This paper presents a hybrid soft computing modeling approach, a neurofuzzy system based on rough set theory and genetic algorithms (GA). To solve the curse of dimensionality problem of neurofuzzy system, rough set is used to obtain the reductive fuzzy rule set. Both the number of condition attributes and rules are reduced. Genetic algorithm is used to obtain the optimal discretization of continuous attributes. The fuzzy system is then represented via an equivalent artificial neural network (ANN). Because the initial parameter of the ANN is reasonable, the convergence of the ANN training is fast. After the rules are reduced, the structure size of the ANN becomes small, and the ANN is not fully weight-connected. The neurofuzzy approach based on RST and GA has been applied to practical application of building a soft sensor model for estimating the freezing point of the light diesel fuel in fluid catalytic cracking unit.     

A systematic neuro-fuzzy modeling framework with application tomaterial property prediction

A systematic neural-fuzzy modeling framework that includes the initial fuzzy model self-generation, significant input selection, partition validation, parameter optimization, and rule-base simplification is proposed in this paper. In this framework, the structure identification and parameter optimization are carried out automatically and efficiently by the combined use of a sell-organization network, fuzzy clustering, adaptive back-propagation learning, and similarity analysis-based model simplification. The proposed neuro-fuzzy modeling approach has been used for nonlinear system identification and mechanical property prediction in hot-rolled steels from construct composition and microstructure data. Experimental studies demonstrate that the predicted mechanical properties have a good agreement with the measured data by using the elicited fuzzy model with a small number of rules.     

Enhanced Fuzzy System Models With Improved Fuzzy Clustering Algorithm

Although traditional fuzzy models have proven to have high capacity of approximating the real-world systems, they have some challenges, such as computational complexity, optimization problems, subjectivity, etc. In order to solve some of these problems, this paper proposes a new fuzzy system modeling approach based on improved fuzzy functions to model systems with continuous output variable. The new modeling approach introduces three features: i) an improved fuzzy clustering (IFC) algorithm, ii) a new structure identification algorithm, and iii) a nonparametric inference engine. The IFC algorithm yields simultaneous estimates of parameters of c-regression models, together with fuzzy c-partitioning of the data, to calculate improved membership values with a new membership function. The structure identification of the new approach utilizes IFC, instead of standard fuzzy c-means clustering algorithm, to fuzzy partition the data, and it uses improved membership values as additional input variables along with the original scalar input variables for two different choices of regression methods: least squares estimation or support vector regression, to determine ldquofuzzy functionsrdquo for each cluster. With novel IFC, one could learn the system behavior more accurately compared to other FSM models. The nonparametric inference engine is a new approach, which uses the alike -nearest neighbor method for reasoning. Empirical comparisons indicate that the proposed approach yields comparable or better accuracy than fuzzy or neuro-fuzzy models based on fuzzy rules bases, as well as other soft computing methods.     

利用数据产生模糊推理系统的自适应学习方法

利用数据设计模糊推理系统分为两个主要内容: 结构辨识和参数优化. 论文首先通过定义隶属度函数和模糊规则简单地给出了一种初始模糊推理系统结构. 然后, 为了提高基于梯度的学习算法的收敛速度、减少振荡, 提出了一种产生模糊推理系统的改进梯度下降方法, 并对算法的收敛性和振荡情况进行了系统分析; 利用这些优化分析结果能够进一步确定在输入变量空间的哪一个区域中模糊规则的密度应该加强, 以及在哪一个输入变量上用于划分其论域的模糊子集的数目应该增加, 从而获得一个新的更精确的模糊推理系统结构. 最后将所提出的方法用于解决非线性函数的逼近问题.     

基于模糊粒子群算法的有向传感器网络路径覆盖策略*

建立有向传感器节点模糊感知模型,利用模糊数据融合规则减少网络不确定区域.对于有向传感器网络路径覆盖问题,提出基于模糊粒子群算法的有向传感器网络路径覆盖增强算法,将n维求解问题转化为一维求解问题,以提高单个传感器节点净覆盖域为目的,提高网络覆盖率.仿真结果表明,对于感知方向可连续调节的有向传感器网络节点,在随机部署情况下与现有算法对比,文中算法能有效提高有向传感器网络路径覆盖率,并且具有较快的收敛速度,延长网络生存期.     

基于目标函数的模糊模型一体化建模

基于模糊集合的模糊模型, 利用模糊推理规则描述复杂、病态、非线性系统是一种有效方法. 本文提出了利用目标函数确定非线性系统的结构和参数的方法. 首先, 通过Gustafson-Kessel(GK)模糊聚类确定模型结构. 然后, 通过目标函数与参数估计一起进行递推计算, 进而实现对模糊模型结构简化, 删除冗余规则. 结构确定过程中采用了UD矩阵分解方法, 大大降低了计算量. 仿真结果证明了提出方法的有效性.     

改进粒子群优化的极限学习机软测量建模方法

工业过程常含有显著的非线性、时变等复杂特性,传统的极限学习机有时无法充分利用数据信息,所建软测量模型预测性能较差。为了提高极限学习机的泛化能力和预测精度,提出一种改进粒子群优化的极限学习机软测量建模方法。首先,利用高斯函数正态分布的特点实现惯性权重的自适应更新,并线性变化学习因子以提高粒子群优化算法的收敛速度和搜索性能;然后将该算法用于优化极限学习机的惩罚系数和核宽,得到一组最优超参数;最后将该方法应用于脱丁烷塔过程软测量建模中。仿真结果表明,优化后的极限学习机模型预测精度有明显的提高,验证了所提方法不仅是可行的,而且具有良好的预测精度和泛化性能。     

A new approach to fuzzy modeling of nonlinear dynamic systems with noise: relevance vector learning mechanism

This paper presents a new fuzzy inference system for modeling of nonlinear dynamic systems based on input and output data with measurement noise. The proposed fuzzy system has a number of fuzzy rules and parameter values of membership functions which are automatically generated using the extended relevance vector machine (RVM). The RVM has a probabilistic Bayesian learning framework and has good generalization capability. The RVM consists of the sum of product of weight and kernel function which projects input space into high dimensional feature space. The structure of proposed fuzzy system is same as that of the Takagi-Sugeno fuzzy model. However, in the proposed method, the number of fuzzy rules can be reduced under the process of optimizing a marginal likelihood by adjusting parameter values of kernel functions using the gradient ascent method. After a fuzzy system is determined, coefficients in consequent part are found by the least square method. Examples illustrate effectiveness of the proposed new fuzzy inference system.