基于随机采样模糊聚类和卡尔曼滤波方法的模糊辨识

提出一种新的基于模糊聚类和卡尔曼滤波方法的模糊辨识算法 .该方法是基于快速模糊聚类 ,计算给定样本在各类中的隶属度 ,并利用卡尔曼滤波方法辨识模糊模型的结论参数 .整个辨识过程与一般的模糊聚类方法 [1 ]相比 ,需要的 CPU时间大大缩短 .最后通过仿真实例验证了该方法的有效性 .     

闭模糊拟阵模糊圈的算法研究

用闭模糊拟阵的基本序列来研究和描述它的模糊圈,找到了从闭模糊拟阵的模糊相关集或模糊独立集计算模糊圈的方法,并给出了相应的算法.     

模糊值函数分析学的结构元方法简介(Ⅰ)——结构元与模糊数运算

简述了模糊值函数分析学在具体工程实践应用中存在的困难和障碍,系统地介绍了模糊结构元方法在模糊值函数分析学中的应用,包括模糊结构元的概念、模糊数的模糊结构元表示形式、基于结构元表达形式的模糊数运算与隶属函数确定.模糊结构元方法将复杂的模糊数运算转化为一类单调有界函数的运算,不仅仅为模糊分析计算的简化提供了工具,同时也为模糊值函数分析学应用的研究开创了一条新的途径.     

单源模糊数的模糊随机有限元方程的解法

在工程实际情况下,有时候可以利用单源模糊数的运算法则,来减少模糊随机有限元方程的计算量.通过推导证明,其计算量仅相当于求解普通的随机有限元方程.为了更好地适应现代工程设计的需要,还提出用模糊随机有限元方程计算结果求结构模糊失效概率的近似方法.     

多源异构数据环境下基于模糊积分融合的公租房退出方法

为有效解决公租房退出问题,提出一种基于多源异构数据的模糊积分融合退出方法,该退出方法首先给出一种多源异构数据的同构化方法,然后针对常用模糊测度确定方法原理复杂、计算困难的问题,提出一种原理简单、易于计算的模糊测度确定方法,该模糊测度确定方法利用单个属性的相对重要程度和两两属性之间的交互度计算单个属性的全局重要程度,最后,根据模糊测度的单调性和有界性通过专家逐级打分计算各子属性集的重要程度。实例验证表明所提出的退出方法可行、有效。     

权值不确定的模糊多属性格序决策方法

针对权值是区间数且指标值以三角模糊数形式给出的模糊多属性决策问题,基于格序决策的理论,提出一种新的格序决策办法.方法通过计算梯形模糊数的中心将TOPSIS方法推广到了模糊数的领域,进而给出一种新的方案排序方法.     

模糊运算和模糊有限元静力控制方程的求解

根据模糊数的区间形式表达和区间运算的性质,给出了模糊数和模糊变量的运算规则.据此并依据区间有限元理论,提出了结构模糊有限元静力控制方程的几种求解方法.方法可根据输入模糊数的隶属函数,给出结构响应量的可能性分布.且计算量小,易于实施.算例分析说明了方法是实用和可行的.     

模糊向量空间中模糊基的判定方法及其维数的计算

本文在（１）的基础上给出了模糊基的另一种构造方法，由此得到了模糊基的判定方法。研究了基的μ值分布状况，最后给出了模糊向量空间维数的计算方法。     

结构的失效可能度及模糊概率计算方法

依据模糊可能性理论,系统地建立含模糊变量时结构的可靠性计算模型。旨在解决模糊结构、模糊-随机结构和模糊状态假设下结构的可靠性计算问题。所建模型可给出模糊结构失效的可能度和模糊-随机结构失效概率的可能性分布。研究表明:对同时含模糊变量和随机变量的混合可靠性计算问题,把失效概率(或可靠度)作为模糊变量,能更客观地反映系统的安全状况。算例分析说明了文中方法的合理性和有效性。     

关于G-V模糊拟阵的对偶模糊拟阵概念的推广

模糊拟阵的研究方法之一就是通过基本序列和导出拟阵序列将模糊拟阵问题转化为普通拟阵问题来进行研究。本文正是采用这个研究方法,主要完成了三项工作:一是给出并证明了闭正规模糊拟阵和正规模糊拟阵的几个充要条件;二是将对偶模糊拟阵概念从闭正规模糊拟阵推广到正规模糊拟阵并讨论了有关性质和计算;三是证明了除正规模糊拟阵外,其他模糊拟阵不存在这样的对偶模糊拟阵。     

Evaluation of fuzzy regression models by fuzzy neural network

In this paper, a novel hybrid method based on fuzzy neural network for approximate fuzzy coefficients (parameters) of fuzzy linear and nonlinear regression models with fuzzy output and crisp inputs, is presented. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples.     

Fuzzy polynomial regression with fuzzy neural networks

Recently, fuzzy linear regression is considered by Mosleh et al. [1]. In this paper, a novel hybrid method based on fuzzy neural network for approximate fuzzy coefficients (parameters) of fuzzy polynomial regression models with fuzzy output and crisp inputs, is presented. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples.     

模糊规划的Hopfield网络方法

首先将一类模糊规划转化为无约束多目标规划，再依据决策者偏好并采用Hopfield网络方法构造该多目标规划的评价函数，从而将模糊规划转化为无约束单目标规划来求解．     

模糊ART神经网络在运动目标识别中的应用

本文在讨论模糊ＡＲＴ神经网络及其算法的基础上，研究和提出了一种三维运动目标识别方法，利用模糊ＡＲＴ神经网络对运动目标的目标侧面图形进行学习和模式识别。模拟实验表明了该方法的有效性。     

Evaluation of fully fuzzy matrix equations by fuzzy neural network

In this paper, a new hybrid method based on fuzzy neural network for approximate solution of fully fuzzy matrix equations of the form AX=D$\mathit{AX}=D$, where A and D are two fuzzy number matrices and the unknown matrix X is a fuzzy number matrix, is presented. Then, we propose some definitions which are fuzzy zero number, fuzzy one number and fuzzy identity matrix. Based on these definitions, direct computation of fuzzy inverse matrix is done using fuzzy matrix equations and fuzzy neural network. It is noted that the uniqueness of the calculated fuzzy inverse matrix is not guaranteed. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate solution of fuzzy matrix equations that supposedly has a unique fuzzy solution, a simple algorithm from the cost function of the fuzzy neural network is proposed. To illustrate the easy application of the proposed method, numerical examples are given and the obtained results are discussed.     

三级倒立摆的T-S型前馈补偿模糊神经网络控制

为解决T akag i-Sugeno型模糊神经网络在控制多变量系统时的规则组合爆炸问题,提出一种误差前馈补偿的模糊神经网络控制方案,有效实现了三级倒立摆的稳定控制。该控制方案适用对状态变量可按性质和重要程度划分的多变量系统的控制,大大减少了模糊神经网络控制器的规则数,有利于利用专家的控制经验,具有良好的鲁棒性和非线性适应能力。     

模糊无限状态自动机及其收敛性

到目前为止,我们所研究的模糊或非模糊的自动机都是有限状态自动机.然而,关于无限状态自动机的定义及它的稳定性和收敛性都没有被讨论过.本文中,我们使用离散的反馈神经网络及网络输出空间划分方法,同时,在梯度更新算法中使用伪梯度方法,给出了模糊无限状态自动机收敛到模糊有限状态自动机的证明.     

三层前向神经网络的一致逼近性

近年来，前向神经网络泛逼近的一致性分析一直为众多学者所重视。本文系统分析三层前向网络对于拟差值保序函数族的一致逼近性，其中，转换函数σ是广义Sigmoidal函数。并将此一致性结果用于建立一类新的模糊神经网络(FNN)，即折线FNN．研究这类网络对于两个给定的模糊函数的逼近性，相关结论在分析折线FNN的泛逼近性时起关键作用。     

Fuzzified neural network based on fuzzy number operations

The fuzzified neural network based on fuzzy number operations is presented as a powerful modelling tool here. We systematically introduce ideas and concepts of a novel neural network based on fuzzy number operations. First we suggest how to compute the results of addition, subtraction, multiplication and division for two fuzzy numbers. Second we propose a learning algorithm, and present some ideas about the choice of fuzzy weights and fuzzy biases and a numerical scheme for the calculation of outputs of the fuzzified neural network. Finally, we show some results of computer simulations.     

A neural fuzzy control system with structure and parameter learning

A general connectionist model, called neural fuzzy control network (NFCN), is proposed for the realization of a fuzzy logic control system. The proposed NFCN is a feedforward multilayered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. The NFCN can be constructed from supervised training examples by machine learning techniques, and the connectionist structure can be trained to develop fuzzy logic rules and find membership functions. Associated with the NFCN is a two-phase hybrid learning algorithm which utilizes unsupervised learning schemes for structure learning and the backpropagation learning scheme for parameter learning. By combining both unsupervised and supervised learning schemes, the learning speed converges much faster than the original backpropagation algorithm. The two-phase hybrid learning algorithm requires exact supervised training data for learning. In some real-time applications, exact training data may be expensive or even impossible to obtain. To solve this problem, a reinforcement neural fuzzy control network (RNFCN) is further proposed. The RNFCN is constructed by integrating two NFCNs, one functioning as a fuzzy predictor and the other as a fuzzy controller. By combining a proposed on-line supervised structure-parameter learning technique, the temporal difference prediction method, and the stochastic exploratory algorithm, a reinforcement learning algorithm is proposed, which can construct a RNFCN automatically and dynamically through a reward-penalty signal (i.e., “good” or “bad” signal). Two examples are presented to illustrate the performance and applicability of the proposed models and learning algorithms.