Web document clustering using a hybrid neural network

The list of documents returned by Internet search engines in response to a query these days can be quite overwhelming. There is an increasing need for organising this information and presenting it in a more compact and efficient manner. This paper describes a method developed for the automatic clustering of World Wide Web documents, according to their relevance to the user’s information needs, by using a hybrid neural network. The objective is to reduce the time and effort the user has to spend to find the information sought after. Clustering documents by features representative of their contents—in this case, key words and phrases—increases the effectiveness and efficiency of the search process. It is shown that a two-dimensional visual presentation of information on retrieved documents, instead of the traditional linear listing, can create a more user-friendly interface between a search engine and the user.     

基于人工鱼群算法的Web用户聚类分析

传统的可伸缩性聚类算法可扩展性不强、处理孤立点的能力较弱。人工鱼群算法是一种基于动物行为的寻求全局最优算法,将人工鱼群算法应用于Web用户聚类,模仿鱼群的觅食、聚群、追尾和随机行为来构造人工鱼,通过鱼群每个个体的局部最优,来找到全局最优值,从而对Web访问用户进行合理聚类。实际运行结果验证了算法的有效性。     

以Web用户关联关系为属性的浏览模式聚类

在Web使用挖掘中,用户浏览模式的聚类结果有助于网站设计者理解Web用户的浏览特点和需要。设计了一种有效的Web浏览模式的聚类方法,网页是否被浏览及网页上的浏览时间反映了用户的浏览兴趣,它们被刻画成等长的用户浏览模式向量中的相应分量,此外,浏览模式之间的关系被刻画并被作为属性加入到该向量中,形成扩展的用户浏览模式向量,对这些向量使用粗糙k-均值法可对用户浏览模式进行有效的聚类。实例和实验分析说明,使用该方法的聚类结果更合理。聚类结果可用于个性化网站的设计。     

Adaptive fuzzy inference neural network

An adaptive fuzzy inference neural network (AFINN) is proposed in this paper. It has self-construction ability, parameter estimation ability and rule extraction ability. The structure of AFINN is formed by the following four phases: (1) initial rule creation, (2) selection of important input elements, (3) identification of the network structure and (4) parameter estimation using LMS (least-mean square) algorithm. When the number of input dimension is large, the conventional fuzzy systems often cannot handle the task correctly because the degree of each rule becomes too small. AFINN solves such a problem by modification of the learning and inference algorithm.     

Supervised adaptive clustering: A hybrid neural network clustering algorithm

A neural network architecture is introduced which implements a supervised clustering algorithm for the classification of feature vectors. The network is selforganising, and is able to adapt to the shape of the underlying pattern distribution as well as detect novel input vectors during training. It is also capable of determining the relative importance of the feature components for classification. The architecture is a hybrid of supervised and unsupervised networks, and combines the strengths of three wellknown architectures: learning vector quantisation, backpro-pagation and adaptive resonance theory. Network performance is compared to that of learning vector quantisation, back-propagation and cascade-correlation. It is found that performance is generally as good as or better than the performance of these other architectures, while training time is considerably shorter. However, the main advantage of the hybrid architecture is its ability to gain insight into the feature pattern space.Nomenclature O _j The output value of thejth unit - I _i Theith component of the input pattern - W _ij The weight of the cluster connection between theith input and thejth unit - B _ij The weight of the shape connection between theith input and thejth unit - N The dimension of the input patterns - v _j The vigilance parameter of thejth unit - v _init The initial vigilance parameter value - v _rate The change in the vigilance parameter value - X _i Theith direction in anN-dimensional coordinate system - T _k The classification tag of thekth unit - C The classification tag of the current input vector - (p) The learning rate at thepth epoch for the cluster weights - p The current epoch - P The total number of epochs - E _k The error associated with thekth unit - The constant learning rate for the shape weights - a _j The age in epochs of thejth unit     

Enhanced neural gas network for prototype-based clustering

In practical cluster analysis tasks, an efficient clustering algorithm should be less sensitive to parameter configurations and tolerate the existence of outliers. Based on the neural gas (NG) network framework, we propose an efficient prototype-based clustering (PBC) algorithm called enhanced neural gas (ENG) network. Several problems associated with the traditional PBC algorithms and original NG algorithm such as sensitivity to initialization, sensitivity to input sequence ordering and the adverse influence from outliers can be effectively tackled in our new scheme. In addition, our new algorithm can establish the topology relationships among the prototypes and all topology-wise badly located prototypes can be relocated to represent more meaningful regions. Experimental results¹on synthetic and UCI datasets show that our algorithm possesses superior performance in comparison to several PBC algorithms and their improved variants, such as hard c-means, fuzzy c-means, NG, fuzzy possibilistic c-means, credibilistic fuzzy c-means, hard/fuzzy robust clustering and alternative hard/fuzzy c-means, in static data clustering tasks with a fixed number of prototypes.     

嵌入式深度神经网络高光谱图像聚类

目的 高光谱图像的高维特性和非线性结构给聚类任务带来了"维数灾难"和线性不可分问题，以往的工作将特征提取过程与聚类过程互相剥离，难以同时优化。为了解决上述问题，提出了一种新的嵌入式深度神经网络模糊C均值聚类方法（EDFCC）。方法 EDFCC算法为了提取更加有效的深层特征，联合优化高光谱图像的特征提取和聚类过程，将模糊C均值聚类算法嵌入至深度自编码器网络中，可以保持两任务联合优化的优势，同时利用深度自编码器网络降维以及逼近任意非线性函数的能力，逐步将原始数据映射到潜在特征空间，提取数据的深层特征。所提方法采用模糊C均值聚类算法约束特征提取过程，学习适用于聚类的高光谱数据深层特征，动态调整聚类指示矩阵。结果 实验结果表明，EDFCC算法在Indian Pines和Pavia University两个高光谱数据集上的聚类精度分别达到了42.95%和60.59%，与当前流行的低秩子空间聚类算法（LRSC）相比分别提高了3%和4%，相比于基于自编码器的数据聚类算法（AEKM）分别提高了2%和3%。结论 EDFCC算法能够从高光谱图像的高维光谱信息中提取更加有效的深层特征，提升聚类精度，并且由于EDFCC算法不需要额外的训练过程，大大提升了聚类效率。     

基于轴突信号理论的神经网络聚类算法

通过借鉴Raju Metherate提出的只有部分脑细胞发出的信号到达了大脑皮层的理论和Stephen R Williams提出的突触信号强度随着离神经细胞主体的距离的加大而减弱的理论,提出了基于轴突信号理论的神经网络聚类算法。此算法在较高维空间中具备和传统竞争神经网络相当甚至更高的聚类准确率;通过对神经网络训练结果的进一步分析可以作为主因素分析和空间降维处理的依据;通过对竞争层神经元之间权重的修正得到类别的自组织关系。最后通过实验证明算法的有效性。     

Adaptive neural network control of coordinated manipulators

In this article, adaptive neural network control of coordinated manipulators is considered in an effort to eliminate the time‐consuming and error prone dynamic modeling process which is necessary for the implementation of conventional adaptive control. After a concise dynamic model in the object coordinate space is developed for the coordinated manipulators, an adaptive neural network controller is presented by combining the techniques of neural network parameterization, adaptive control, and sliding mode control. It can be shown that the motion tracking errors converge to zero asymptotically whereas the internal force tracking error remains bounded and can be made arbitrarily small. Numerical simulations are conducted to show the effectiveness of the proposed method. ©1999 John Wiley & Sons, Inc.     

Ad Hoc网络自适应安全加权分簇算法

为解决Ad Hoc网络分簇过程中恶意节点被选为簇首带来的安全隐患,保障Ad Hoc网络的正确分簇和稳定运行,提出基于节点相关度、相对移动性、剩余能量值、安全评估度量值多方面因素的自适应安全加权分簇算法。安全评估度量参数由外部入侵检测系统和内部节点信任度共同计算得到,确保安全因素在分簇过程中的准确性;基于该算法给出相应分簇管理过程。仿真结果表明,该算法能够改善分簇性能,提高Ad Hoc网络的安全性。     

一种改进的ART2网络学习算法

分析了现有ART2网络存在的问题,提出了一种改进的ART2算法。该算法首先利用样本数据自身来初始化权值,然后按照同一类中的数据点到其聚类中心的距离之和越小(即类内偏差越小),聚类效果越好的原则来设计特征表示场和类别表示场之间的权值修正公式,最后通过比较输入样本和聚类中心的模来有效地利用模式的幅度信息。分析证明了该算法不仅能有效解决模式漂移问题、充分利用幅度信息,而且能提高聚类速度。     

监督模糊聚类神经网络及其应用

为在面料成衣之前客观评价其缝纫性能,提出了一种基于监督模糊聚类客观评价方法.通过引入输出空间对FCM聚类算法进行改进,同时反映输入空间的聚类特征和输出空间的逼近特性.用FAST系统测量服装面料的力学性能指标,运用核主成分法对所测指标进行分析,提取5个核主成分作为神经网络的输入.实验结果表明,系统可以根据中厚型棉织物的不同结构及物理性能快速准确地给出该织物成衣后的缝纫性能评价指标.     

General fuzzy min-max neural network for clustering andclassification

This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993). The GFMM method combines supervised and unsupervised learning in a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering, pure classification, or hybrid clustering classification. It exhibits a property of finding decision boundaries between classes while clustering patterns that cannot be said to belong to any of existing classes. Similarly to the original algorithms, the hyperbox fuzzy sets are used as a representation of clusters and classes. Learning is usually completed in a few passes and consists of placing and adjusting the hyperboxes in the pattern space; this is an expansion-contraction process. The classification results can be crisp or fuzzy. New data can be included without the need for retraining. While retaining all the interesting features of the original algorithms, a number of modifications to their definition have been made in order to accommodate fuzzy input patterns in the form of lower and upper bounds, combine the supervised and unsupervised learning, and improve the effectiveness of operations. A detailed account of the GFMM neural network, its comparison with the Simpson's fuzzy min-max neural networks, a set of examples, and an application to the leakage detection and identification in water distribution systems are given     

基于改进型聚类神经网络的图像分割

将聚类网络用于非监督的图像分割,提出了竞争层神经元的动态调整机制和返回式的非重复训练学习方案,实现了聚类数的自适应增加,解决了随机生成权值矩阵产生的死点问题,提高了算法的收敛性能。实验结果表明,改进的聚类网络的图像分割结果优于C-均值聚类算法和通常的聚类网络。     

基于子图分解的图聚类神经网络

聚类是机器学习的核心任务之一,其主要目的是将无标签数据中的不同簇数据进行分离。深度聚类算法使用深度神经网络联合优化聚类目标与特征提取,极大地提高了聚类性能。图聚类是深度聚类领域近两年研究的一个重要分支,其在处理图结构数据上有极大的优势。提出一种新的图聚类方案：基于子图分解的图聚网络,该模型在图自编码器的基础上通过构建多个子图,并在子图的嵌入空间中加以组稀疏约束达到最终的聚类目的。     

Speaker indexing using neural network clustering of vowel spectra

Speaker indexing refers to the process of separating speakers within a recording and assigning indices to each unique speaker. This paper describes a new speaker indexing algorithm which dynamically generates and trains a neural network to model each postulated speaker found within a recording. Each neural network is trained to differentiate the vowel spectra of one specific speaker from all other speakers. A method for combining speaker indexing and other annotations of a recording in a general framework is also presented. The speaker indexing system is currently being incorporated into several application systems in the Speech Group at the MIT Media Lab.     

Adaptive motion control using neural network approximations

In this paper, we present a new adaptive technique for tracking control of mechanical systems in the presence of friction and periodic disturbances. Radial Basis Functions (RBFs) are used to compensate for the effects of nonlinearly occurring parameters in the friction and periodic disturbance model. Theoretical analysis, such as stability and transient performance, is provided. Furthermore, the performance of the adaptive RBF controller and its non-adaptive counterpart are compared.     

Adaptive image interpolation using probabilistic neural network

This paper proposes an image interpolation model based on probabilistic neural network (PNN). The method adjusts automatically the smoothing parameters for varied smooth/edge image region, and takes into consideration both smoothness (flat region) and sharpness (edge region) characteristics at the same model. A single neuron, combined with PSO training, is used for sharpness/smoothness adaptation. Finally, we report the performance of these newly proposed methods in other image interpolation method.     

Adaptive driver model using a neural network

An adaptive driver model for longitudinal movements of a vehicle has been developed. It incorporates a conventional feedback brake controller, and both fixed and adaptive neural network controllers to produce the throttle demand. It has been interfaced with a vehicle model in a Simulink environment, and simulation studies indicate a high level of performance. Implementation of the adaptive driver model within a real-time environment has also been realized successfully. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

Adaptive neural complementary sliding-mode control via functional-linked wavelet neural network

Chaos control can be applied in the vast areas of physics and engineering systems, but the parameters of chaotic system are inevitably perturbed by external inartificial factors and cannot be exactly known. This paper proposes an adaptive neural complementary sliding-mode control (ANCSC) system, which is composed of a neural controller and a robust compensator, for a chaotic system. The neural controller uses a functional-linked wavelet neural network (FWNN) to approximate an ideal complementary sliding-mode controller. Since the output weights of FWNN are equipped with a functional-linked type form, the FWNN offers good learning accuracy. The robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Without requiring preliminary offline learning, the parameter learning algorithm can online tune the controller parameters of the proposed ANCSC system to ensure system stable. Finally, it shows by the simulation results that favorable control performance can be achieved for a chaotic system by the proposed ANCSC scheme.