熵约束广义学习矢量量化神经网络和软竞争学习算法

结合广义学习矢量量化神经网络的思想和信息论中的极大熵原理,提出了一种熵约束 广义学习矢量量化神经网络,利用梯度下降法导出其学习算法,该算法是软竞争格式的一种推 广.由于亏损因子和尺度函数被定义为同一个模糊隶属度函数,它可以有效地克服广义学习矢 量量化网络的模糊算法存在的问题.文中还给出熵约束广义学习矢量量化网络及其软竞争学习 算法的许多重要性质,以此为依据,讨论拉格朗日乘子的选取规则.     

基于Mean-shift算法与模糊熵的图像平滑

为解决均值漂移算法的滤波核带宽的选择问题,通过分析模糊理论的模糊隶属度函数和Mean-shift算法的核函数的定义,指出可将模糊隶属度函数作为Mean-shift的核函数。由此定义新隶属度函数作为表示灰度信息的核函数,应用Mean-shift算法对一幅混有噪声的细胞图像进行平滑处理。通过实验表明该方法能到达较好的平滑效果且不需要选择核带宽hr。     

一种改进的模糊边缘检测快速算法

比较全面地分析了 Pal.King模糊边缘检测算法的缺陷 ,提出了一种新的快速模糊边缘检测算法。该算法不仅克服了 Pal.King模糊检测算法定义的不足 ,简化了复杂的变换和逆变换运算 ,而且针对 Pal.King算法中对隶属度阈值设置为固定值的不足 ,提出了自动确定模糊增强变换中最佳隶属度阈值的算法 ,并在此基础上实现模糊增强函数中增强阈值的自动获取。仿真结果证明 ,该算法效率高、提取边缘精细、适用面广 ,是一种很有实用价值的图像处理算法     

基于混合聚类算法的模糊函数系统辨识方法

针对传统模糊系统存在的结构难以确定和参数辨识复杂的问题,提出了一种基于混合聚类算法的模糊函数系统辨识算法.与一般的模糊函数系统相比,混合聚类算法结合模糊C均值和模糊C回归模型聚类算法的样本距离.在模型预测部分,采用高斯函数计算每个输入变量的隶属度,利用输入变量隶属度的模糊化算子得到输入向量的隶属度.应用于Box-Jenkins煤气炉数据、一个双入单出的非线性系统和Mackey-Glass混沌时间序列数据的试验结果表明,本文算法具有很好的辨识效果,从而验证了本文算法的有效性与实用性.     

二型模糊系统的规则提取算法

模糊规则提取是建立二型模糊系统需要解决的关键问题.提出一种改进的基于c均值模糊聚类算法(FCM)的二型模糊规则提取方法.该方法借助于二型模糊集主隶属度函数的期望与次隶属度函数值之间的联系,能克服已有算法忽略二型模糊集次隶属度函数对模糊聚类结果的影响.仿真实例表明.该算法能成功地提取二型模糊规则,比FCMV算法具有更好的性能和收敛性.     

模糊对向传播神经网络及其应用

通过把对向传播(CP)神经网络的竞争层神经元的输出函数定义为模糊隶属度函 数,提出了模糊对向传播(FCP)神经网络.该网络是CP网络的推广,它不仅能有效克服CP 存在的问题,而且具有全局函数逼近能力.在结构上,FCP网络同径向基函数(RBF)网络是等 价的.实际上,它是一种RBF网络,而且还是一种模糊基函数网络.FCP在时间序列预测中的 应用表明,FCP不仅在学习精度上,而且在泛化能力方面较之CP和RBF均有较大的改善.     

一种挖掘模糊关联规则的新方法

提出了一种基于改进的K-means聚类算法上的自动确定样本数据隶属度函数的新方法,并在此基础上结合Apriori算法,提出了一种挖掘模糊关联规则的新算法。与现有的同类算法相比,现有的方法均需随机地确定初始的聚类中心,往往得出有悖于实际的隶属度函数,从而影响了整个模糊关联规则的提取结果。该算法克服了这一缺点,提高了模糊关联规则的提取结果的正确性。     

一种改进的自适应模糊卡尔曼滤波算法

针对常规卡尔曼滤波（KF）处理小噪声和变化噪声不足,提出了一种改进的自适应模糊卡尔曼滤波[1]（IAF-KF）算法。该算法根据模糊推理输入量的变化特点建立一个新的非线性隶属度函数,取代了常用的三角形线形隶属度函数;然后利用模糊化后的等级和隶属度构造了补偿调节函数（CAF）,用于调节卡尔曼滤波算法中的误差,提高实际测量误差与理论测量误差间的匹配程度。仿真实验表明,较之传统的卡尔曼滤波,该方法在小噪声和变化的噪声情形下有效的克服了稳态误差,同时降低了模糊卡尔曼滤波算法的复杂程度。     

一种挖掘模糊相似关联规则的新方法

提出了一种基于自组织特征映射(SOFM)网络的自动确定样本数据隶属度函数的新方法,并在此基础上根据相似性的概念,给出了相似度的计算公式,结合Apriori算法,提出了一种挖掘模糊相似关联规则的新算法。与现有的同类算法相比,现有的方法均需人为地确定隶属度函数,带有一定的主观性,尤其当数据结构较复杂时,隶属度函数难以确定;该算法克服了这一缺点,同时减少了冗余规则。     

弧焊过程神经网络模糊控制

提出一种将FLC与神经网络技术相结合的方法对钨极氩弧焊（GTAW）过程进行控制,它克服了模糊规则产生对专家的依赖及模糊集非自适应性的问题。隶属函数的自适应及模糊规则的自组织通过神经网络的自学习和竞争获得。该方法实现了弧焊过程中模糊规则的自动确定和隶属度函数在线调度。 以GTAW过程焊缝几何参数调节为对象,验证了算法的有效性。计算机仿真表明,采用该方法的系统性能有较大的提高。     

Fuzzy algorithms for learning vector quantization

This paper presents the development of fuzzy algorithms for learning vector quantization (FALVQ). These algorithms are derived by minimizing the weighted sum of the squared Euclidean distances between an input vector, which represents a feature vector, and the weight vectors of a competitive learning vector quantization (LVQ) network, which represent the prototypes. This formulation leads to competitive algorithms, which allow each input vector to attract all prototypes. The strength of attraction between each input and the prototypes is determined by a set of membership functions, which can be selected on the basis of specific criteria. A gradient-descent-based learning rule is derived for a general class of admissible membership functions which satisfy certain properties. The FALVQ 1, FALVQ 2, and FALVQ 3 families of algorithms are developed by selecting admissible membership functions with different properties. The proposed algorithms are tested and evaluated using the IRIS data set. The efficiency of the proposed algorithms is also illustrated by their use in codebook design required for image compression based on vector quantization.     

A methodology for constructing fuzzy algorithms for learning vectorquantization

This paper presents a general methodology for the development of fuzzy algorithms for learning vector quantization (FALVQ). The design of specific FALVQ algorithms according to existing approaches reduces to the selection of the membership function assigned to the weight vectors of an LVQ competitive neural network, which represent the prototypes. The development of a broad variety of FALVQ algorithms can be accomplished by selecting the form of the interference function that determines the effect of the nonwinning prototypes on the attraction between the winning prototype and the input of the network. The proposed methodology provides the basis for extending the existing FALVQ 1, FALVQ 2, and FALVQ 3 families of algorithms. This paper also introduces two quantitative measures which establish a relationship between the formulation that led to FALVQ algorithms and the competition between the prototypes during the learning process. The proposed algorithms and competition measures are tested and evaluated using the IRIS data set. The significance of the proposed competition measure is illustrated using FALVQ algorithms to perform segmentation of magnetic resonance images of the brain.     

A systematic method for design of multivariable fuzzy logic controlsystems

This paper proposes a systematic method to design a multivariable fuzzy logic controller for large-scale nonlinear systems. In designing a fuzzy logic controller, the major task is to determine fuzzy rule bases, membership functions of input/output variables, and input/output scaling factors. In this work, the fuzzy rule base is generated by a rule-generated function, which is based on the negative gradient of a system performance index; the membership functions of isosceles triangle of input/output variables are fixed in the same cardinality and only the input/output scaling factors are generated from a genetic algorithm based on a fitness function. As a result, the searching space of parameters is narrowed down to a small space, the multivariable fuzzy logic controller can quickly constructed, and the fuzzy rules and the scaling factors can easily be determined. The performance of the proposed method is examined by computer simulations on a Puma 560 system and a two-inverted pendulum system     

An axiomatic approach to soft learning vector quantization andclustering

This paper presents an axiomatic approach to soft learning vector quantization (LVQ) and clustering based on reformulation. The reformulation of the fuzzy c-means (FCM) algorithm provides the basis for reformulating entropy-constrained fuzzy clustering (ECFC) algorithms. According to the proposed approach, the development of specific algorithms reduces to the selection of a generator function. Linear generator functions lead to the FCM and fuzzy learning vector quantization algorithms while exponential generator functions lead to ECFC and entropy-constrained learning vector quantization algorithms. The reformulation of LVQ and clustering algorithms also provides the basis for developing uncertainty measures that can identify feature vectors equidistant from all prototypes. These measures are employed by a procedure developed to make soft LVQ and clustering algorithms capable of identifying outliers in the data set. This procedure is evaluated by testing the algorithms generated by linear and exponential generator functions on speech data.     

DCT-Yager FNN: a novel Yager-based fuzzy neural network with the discrete clustering technique.

Earlier clustering techniques such as the modified learning vector quantization (MLVQ) and the fuzzy Kohonen partitioning (FKP) techniques have focused on the derivation of a certain set of parameters so as to define the fuzzy sets in terms of an algebraic function. The fuzzy membership functions thus generated are uniform, normal, and convex. Since any irregular training data is clustered into uniform fuzzy sets (Gaussian, triangular, or trapezoidal), the clustering may not be exact and some amount of information may be lost. In this paper, two clustering techniques using a Kohonen-like self-organizing neural network architecture, namely, the unsupervised discrete clustering technique (UDCT) and the supervised discrete clustering technique (SDCT), are proposed. The UDCT and SDCT algorithms reduce this data loss by introducing nonuniform, normal fuzzy sets that are not necessarily convex. The training data range is divided into discrete points at equal intervals, and the membership value corresponding to each discrete point is generated. Hence, the fuzzy sets obtained contain pairs of values, each pair corresponding to a discrete point and its membership grade. Thus, it can be argued that fuzzy membership functions generated using this kind of a discrete methodology provide a more accurate representation of the actual input data. This fact has been demonstrated by comparing the membership functions generated by the UDCT and SDCT algorithms against those generated by the MLVQ, FKP, and pseudofuzzy Kohonen partitioning (PFKP) algorithms. In addition to these clustering techniques, a novel pattern classifying network called the Yager fuzzy neural network (FNN) is proposed in this paper. This network corresponds completely to the Yager inference rule and exhibits remarkable generalization abilities. A modified version of the pseudo-outer product (POP)-Yager FNN called the modified Yager FNN is introduced that eliminates the drawbacks of the earlier network and yi- elds superior performance. Extensive experiments have been conducted to test the effectiveness of these two networks, using various clustering algorithms. It follows that the SDCT and UDCT clustering algorithms are particularly suited to networks based on the Yager inference rule.     

基于划分的模糊聚类算法

在众多聚类算法中，基于划分的模糊聚类算法是模式识剐中最常用的算法类型之一．至今，献中仍不断有关于基于划分的模糊聚类算法的研究成果出现．为了能更为系统和深入地了解这些聚类算法及其性质，本从改变度量方式、改变约束条件、在目标函数中引入熵以及考虑对聚类中心进行约束等几个方面，对在C-均值算法的基础上得到的基于划分的模糊聚类算法作了综述和评价，对各典型算法的优缺点进行了实验比较分析．指出标准FCM算法被广泛应用的原因之一是它对数据的比例变化具有鲁棒性，而其他类似的算法对这种比例变化却很敏感．并以极大熵方法为例进行了比较实验．最后总结了基于划分的模糊聚类算法普遍存在的问题及其发展前景。     

Soft learning vector quantization and clustering algorithms basedon ordered weighted aggregation operators

This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators. An axiomatic approach provides conditions for selecting aggregation operators that lead to admissible reformulation functions. Minimization of admissible reformulation functions based on ordered weighted aggregation operators produces a family of soft LVQ and clustering algorithms, which includes fuzzy LVQ and clustering algorithms as special cases. The proposed LVQ and clustering algorithms are used to perform segmentation of magnetic resonance (MR) images of the brain. The diagnostic value of the segmented MR images provides the basis for evaluating a variety of ordered weighted LVQ and clustering algorithms.     

Supervised Pseudo Self-Evolving Cerebellar algorithm for generating fuzzy membership functions

The proper generation of fuzzy membership function is of fundamental importance in fuzzy applications. The effectiveness of the membership functions in pattern classifications can be objectively measured in terms of interpretability and classification accuracy in the conformity of the decision boundaries to the inherent probabilistic decision boundaries of the training data. This paper presents the Supervised Pseudo Self-Evolving Cerebellar (SPSEC) algorithm that is bio-inspired from the two-stage development process of the human nervous system whereby the basic architecture are first laid out without any activity-dependent processes and then refined in activity-dependent ways. SPSEC first constructs a cerebellar-like structure in which neurons with high trophic factors evolves to form membership functions that relate intimately to the probability distributions of the data and concomitantly reconcile with defined semantic properties of linguistic variables. The experimental result of using SPSEC to generate fuzzy membership functions is reported and compared with a selection of algorithms using a publicly available UCI Sonar dataset to illustrate its effectiveness.