基于T-S模糊模型的神经网络的系统辨识

基于T-S模糊模型,提出了利用神经网络实现非线性系统的辨识。首先,利用一种无监督的聚类算法分析输入输出数据生成初始的结构模型,确定系统的模糊空间和模糊规则数,构造神经网络辨识模型前提参数,使前提参数自适应变化,有较好的自学习能力和优化能力,采用最小二乘法取得结论参数。仿真结果验证了该方法是有效和可行的。     

模糊神经网络的结构自组织算法及应用

提出了一种新的模糊神经网络自组织算法,该算法能够基于输入输出数据自动进行结构辨识和参数辨识.首先采用一种自组织聚类方法建立起网络的结构和各参数的初值,然后采用监督学习来优化网络参数.通过对非线性函数逼近的分析,明了该自组织算法的有效性,并与其他算法作了比较.最后,以某污水处理厂的实际运行数据为对象,应用该模糊神经网络建立了活性污泥系统出水水质预测模型,仿真结果表明.该模型能够对污水处理系统出水水质进行较好的预测.     

基于一种新模糊模型的非线性系统模糊辨识

提出一种基于新的模糊模型和加权递推最小二乘算法 (WRLSA)的非线性系统模糊辨识方法.新型的具有插值能力的模糊系统可以通过学习从输入输出采样数据中提取MISO系统模糊规则,它继承了Sugeno模型及其变化形式的许多优点.采用相应的模糊隶属函数,使得被辨识的模型可用若干局部线性模型来表示,然后利用WRLSA拟合这些线性模型.给出了详细的模糊辨识算法,为了验证该辨识方法的有效性,还给出了对熟知的Box-Jenkins数据的辨识结果.     

基于SOM规则自动生成的模糊神经网络模型

1 引言模糊系统建模一般将经过系统结构辨识和系统参数估计两个阶段。在辨识阶段,主要决定输入变量及其相互关系、模糊规则数、输入输出空间划分和系统参数的初值;在估计阶段,主要用来调整系统参数以使得系统的输出与目标输出的差值尽可能小。对于系统参数估计阶段的参数调整,人们已提出一些自动方法。对于系统结构辨识阶段,也产生了如模板法、聚类法和决策树法等,但这些方法一般都需要人工干预。其中模糊规则的生成与调整以及隶属度函数的选取是系统结构辨识阶段的主要问题,文提出了用神经网络自动生成模糊规则并进行隶属度形状调整,从而构成模糊神经网络。Wang提出自动分割输入空间的方法,Lin提出三阶段学习算法的模糊神经网络。     

一种基于模糊分类的模糊神经网络辨识方法

针对非线性辨识问题,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)。首先,基于模糊竞争学习算法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。其次,利用卡尔曼滤波算法在线辨识AFNN的后件参数。AFNN具有结构简洁,逼近能力强,能够显著提高辨识精度,并且辨识的模糊模型简单有效。最后,将该AFNN用于非线性系统的模糊辨识,仿真结果验证了该方法的有效性。     

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．     

基于遗传算法的模糊逻辑系统滚动学习方法

基于遗传算法的模糊逻辑系统滚动学习方法是一种局部优化策略，它针对参数空间内的局部区域，由区域内输入输出数据对提取模糊规则，并对规则参数进行调整，参数学习采用差分进行化算法，采用既包含区域内数据，又包含区域外数据的滚动数据窗技术，保证对局部模糊规则的参数调节不致影响系统在相邻区域的逼近性能，算法在保证精度的前提下大大减少了计算量，使遗传算法能应用于模糊逻辑系统的在线学习。     

基于模糊神经网络的飞机某系统故障诊断研究

提出一种基于模糊神经网络的飞机某系统故障诊断方法。利用改进的模糊C均-值聚类算法进行结构辨识,从而自动获得模糊规则库,并得到模糊模型的初始参数;然后生成与之相匹配的初始模糊神经网络,并通过学习算法训练网络来进行参数辨识,得到一个精确的模糊模型。将该系统地面实测数据作为样本数据,建立起了基于模糊神经网络的飞机某系统故障诊断模型。最后对该模型进行测试与分析,结果表明该方法具有抗噪、抗敏感、诊断准确度高等优点。     

模糊竞争神经网络在线辨识及在CSTR中的应用

基于改进的T-S模糊模型构造了一种自适应模糊竞争神经网络模型(FCNN),给出了网络的连接结构和学习算法。它依据模糊竞争学习算法确定系统的模糊空间和模糊规则数,得出每个样本对每条规则的适用程度,并利用卡尔曼滤波算法在线辨识FCNN的后件参数。将其应用于化工过程连续搅拌反应器(CSTR)的建模中,仿真结果表明,FCNN具有结构简洁、收敛速度快、辨识精度高等特点,可当作复杂系统建模的一种有效手段。     

Three new fuzzy neural networks learning algorithms based on clustering, training error and genetic algorithm

Three new learning algorithms for Takagi-Sugeno-Kang fuzzy system based on training error and genetic algorithm are proposed. The first two algorithms are consisted of two phases. In the first phase, the initial structure of neuro-fuzzy network is created by estimating the optimum points of training data in input-output space using KNN (for the first algorithm) and Mean-Shift methods (for the second algorithm) and keeps adding new neurons based on an error-based algorithm. Then in the second phase, redundant neurons are recognized and removed using a genetic algorithm. The third algorithm then builds the network in one phase using a modified version of error algorithm used in the first two methods. The KNN method is shown to be invariant to parameter K in KNN algorithm and in two simulated examples outperforms other neuro-fuzzy approaches in both performance and network compactness.     

An adaptive neural fuzzy filter and its applications

A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper. The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules. The adaptation here includes the construction of fuzzy if-then rules (structure learning), and the tuning of the free parameters of membership functions (parameter learning). In the structure learning phase, fuzzy rules are found based on the matching of input-output clusters. In the parameter learning phase, a backpropagation-like adaptation algorithm is developed to minimize the output error. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially, and both the structure learning and parameter learning are performed concurrently as the adaptation proceeds. However, if some linguistic information about the design of the filter is available, such knowledge can be put into the ANFF to form an initial structure with hidden nodes. Two major advantages of the ANFF can thus be seen: 1) a priori knowledge can be incorporated into the ANFF which makes the fusion of numerical data and linguistic information in the filter possible; and 2) no predetermination, like the number of hidden nodes, must be given, since the ANFF can find its optimal structure and parameters automatically     

A TSK-type recurrent fuzzy network for dynamic systems processingby neural network and genetic algorithms

In this paper, a TSK-type recurrent fuzzy network (TRFN) structure is proposed. The proposal calls for the design of TRFN by either neural network or genetic algorithms depending on the learning environment. A recurrent fuzzy network is described which develops from a series of recurrent fuzzy if-then rules with TSK-type consequent parts. The recurrent property comes from feeding the internal variables, derived from fuzzy firing strengths, back to both the network input and output layers. In this configuration, each internal variable is responsible for memorizing the temporal history of its corresponding fuzzy rule. The internal variable is also combined with external input variables in each rule's consequence, which shows an increase in network learning ability. TRFN design under different learning environments is next advanced. For problems where supervised training data is directly available, TRFN with supervised learning (TRFN-S) is proposed, and a neural network (NN) learning approach is adopted for TRFN-S design. An online learning algorithm with concurrent structure and parameter learning is proposed. With flexibility of partition in the precondition part, and outcome of TSK-type, the TRFN-S displays both small network size and high learning accuracy. For problems where gradient information for NN learning is costly to obtain or unavailable, like reinforcement learning, TRFN with Genetic learning (TRFN-G) is put forward. The precondition parts of TRFN-G are also partitioned in a flexible way, and all free parameters are designed concurrently by genetic algorithm. Owing to the well-designed network structure of TRFN, TRFN-G, like TRFN-S, is characterized by high learning accuracy. To demonstrate the superior properties of TRFN, TRFN-S is applied to dynamic system identification and TRFN-G to dynamic system control. By comparing the results to other types of recurrent networks and design configurations, the efficiency of TRFN is verified     

Knowledge Incorporation into Neural Networks From Fuzzy Rules

The incorporation of prior knowledge into neural networks can improve neural network learning in several respects, for example, a faster learning speed and better generalization ability. However, neural network learning is data driven and there is no general way to exploit knowledge which is not in the form of data input-output pairs. In this paper, we propose two approaches for incorporating knowledge into neural networks from fuzzy rules. These fuzzy rules are generated based on expert knowledge or intuition. In the first approach, information from the derivative of the fuzzy system is used to regularize the neural network learning, whereas in the second approach the fuzzy rules are used as a catalyst. Simulation studies show that both approaches increase the learning speed significantly.     

正规化模糊神经网络及在手写体汉字识别中的应用

为改善手写体汉字识别的性能,提出了一种基于正规化模糊神经网络的识别方法。针对网络结构的优化问题给出了网络模型的规则层节点的选取方法和相应的反传播学习规则。该算法能够充分利用专家制订的“if-then”规则,完善网络的推理结构,提高网络的识别能力,减少噪声因素的影响。实验表明此方法对手写体汉字识别问题具有良好的适应性和实用性。该方法指出了一条进一步提高手写体汉字系统性能的新途径。     

基于进化式模糊神经网络的时间序列预测系统

文章介绍了一种基于进化式模糊神经网络时间预测系统，它是一种快速自适应的局部学习模型；进化式模糊神经网络是一个特殊类型的神经网络，它能通过进化其结构和参数来容纳新的数据。文章重点介绍了网络结构、学习方法及创建、修剪、聚合规则节点的算法；实验结果表明：模糊隶属函数的个数，规则的修剪和聚合等训练参数，与网络的行为和预测结果有很重要的关系。     

Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction

This paper presents the development of fuzzy wavelet neural network system for time series prediction that combines the advantages of fuzzy systems and wavelet neural network. The structure of fuzzy wavelet neural network (FWNN) is proposed, and its learning algorithm is derived. The proposed network is constructed on the base of a set of TSK fuzzy rules that includes a wavelet function in the consequent part of each rule. A fuzzy c-means clustering algorithm is implemented to generate the rules, that is the structure of FWNN prediction model, automatically, and the gradient-learning algorithm is used for parameter identification. The use of fuzzy c-means clustering algorithm with the gradient algorithm allows to improve convergence of learning algorithm. FWNN is used for modeling and prediction of complex time series and prediction of foreign-exchange rates. Exchange rates are dynamic process that changes every day and have high-order nonlinearity. The statistical data for the last 2 years are used for the development of FWNN prediction model. Effectiveness of the proposed system is evaluated with the results obtained from the simulation of FWNN-based systems and with the comparative simulation results of previous related models.     

A Fuzzy Modelling Approach Using Hierarchical Neural Networks

A simple and effective fuzzy modelling approach is presented in this paper. A three-layer hierarchical clustering neural network is developed to build fuzzy rule-based models from numerical data. Differing from existing clustering-based methods, in this approach the structure identification of the fuzzy model is implemented on the basis of a class of sub-clusters created by a self-organising network instead of on raw data. By combined use of unsupervised and supervised learning, both structure identification and parameter optimisation of the fuzzy model can be carried out automatically. The simulation results show that the proposed method can provide good model structure for fuzzy modelling and has high computing efficiency.