基于遗传策略和神经网络的非监督分类方法

文章提出了一种新的基于遗传策略和模糊ART(adaptive resonance theory)神经网络的非监督分类方法.首先,利用原有的训练样本对模糊ART神经网络进行非监督训练,然后,采用遗传策略为模糊ART神经网络增加各类族边界邻域内的训练样本点,再对模糊ART神经网络进行有监督训练.这种方法解决了训练样本在较少条件下的ART系列神经网络的学习与分类问题,提高了ART系列神经网络的分类性能,并扩展了其应用范围.     

一种新的ART网络遥感影像分类方法

本文提出了一种基于模糊算子的ART2A-C遥感影像分类算法。算法结合原有几种高性能的ART网络对传统ART2A-C网络做了改进。论文分别利用现有网络和改进算法对遥感影像作了聚类,实验结果表明新算法的分类性能明显优于原算法。     

Fault diagnosis of rolling bearing based on optimized soft competitive learning Fuzzy ART and similarity evaluation technique

In this work, a new classification method called Soft Competitive Learning Fuzzy Adaptive Resonance Theory (SFART) is proposed to diagnose bearing faults. In order to solve the misclassification caused by the traditional Fuzzy ART based on hard competitive learning, a soft competitive learning ART model is established using Yu’s norm similarity criterion and lateral inhibition theory. The proposed SFART is based on Yu’s norm similarity criterion and soft competitive learning mechanism. In SFART, Yu’s similarity criterion and the lateral inhibition theory were employed to measure the proximity and select winning neurons, respectively. To further improve the classification accuracy, a feature selection technique based on Yu’s norms is also proposed. In addition, Particle Swarm Optimization (PSO) is introduced to optimize the model parameters of SFART. Meanwhile, the validity of the feature selection technique and parameter optimization method is demonstrated. Finally, fuzzy ART/ ARTMAP (FAM) as well as the feasibility of the proposed SFART algorithm are validated by comparing the diagnosis effectiveness of the proposed algorithm with the classic Fuzzy c-means (FCM), Fuzzy ART and fuzzy ARTMAP (FAM).     

ART算法快速图像重建研究

讨论了影响ART(Algebraic Reconstruction Technique)算法重建速度和重建质量的主要因素,包括投影数据访问方式、松弛因子和投影系数等。针对投影系数的计算占绝大部分重建时间,成为制约ART算法的瓶颈,提出了一种快速、实时的投影系数计算方法。该方法通过一个距离参数来确定与射线相交的网格编号并计算出相交长度,距离参数采用增量计算,极大地节省了时间。实验结果表明该文提出的方法非常有效,与Siddon算法相比,在保证图像质量不受损失的前提下取得了6倍以上的重建加速比。     

引入遗忘机制的ART2改进模型

论文针对ART2网络学习与记忆的特点,在原始ART2的基础上提出具有遗忘机制的改进模型,并开发了相应的MATLAB程序。改进模型解决了原始ART2网络权值学习的随机偏移问题,有效地过滤了噪声,提高了分类结果的稳定性,降低了空间存储消耗。文章进一步运用改进模型对典型输入样本进行了分类,得到了理想的结果。     

DESIGN OF A PREDICTION MODEL FOR CEMENT ROTARY KILN USING WAVELET PROJECTION FUZZY INFERENCE SYSTEM

In a cement factory, a rotary kiln is the most complex component and it plays a key role in the quality and quantity of the final product. This system involves complex nonlinear dynamic equations that have not been completely worked out yet. In conventional modeling procedures, a large number of the involved parameters are crossed out and an approximation model is presented instead. Therefore, the performance of the obtained model is very important and an inaccurate model may cause many problems in the design of a controller. This study presents a Takagi-Sugeno (TS)-type fuzzy system called a wavelet projection fuzzy inference system (WPFIS) in which a dimension reduction section is used at the input stage of the fuzzy system. In order to clarify the structure of the extracted features, structural learning with forgetting (SLF) based on Minkowski norms is proposed. In addition, gradient descent (GD) was used as a training algorithm. The results show that the proposed method has higher performance in comparison with conventional models. The data collected from Saveh White Cement Company were used in our simulations.     

基于云的多光谱遥感影像边缘检测研究

提出一种基于云的多光谱遥感影像边缘检测算法。该算法依据矢量角相似性准则并结合邻域关系进行图像区域生长,在此基础之上根据影像的波段建立多维云模型,将待处理对象映射到多个云空间,通过逻辑运算生成边界云并进行多维向量的综合。构建边缘模糊特征平面,在条件概率和模糊划分熵的基础上,通过最大模糊熵原则确定最优阈值,对图像模糊边界进行提取。试验结果表明,该算法在多光谱遥感影像中能取得较好检测效果。     

基于ART2网络聚类分析的数据融合算法研究

人工神经网络为数据融合提供了新的理论方法和技术手段,在数据融合的各个方面具有广泛的应用前景。自适应共振理论(ART)是一种无监督神经网络,能够实现对输入的任何模拟信号的自动识别和分类。据此提出了一种以ART2网络聚类分析为核心的数据融合算法,探讨了ART2网络用于特征层数据融合实现模式识别/分类的机理,最后给出该算法在一例模式识别/分类中的应用-实现对工业控制系统中设备运行状态的实时监测和故障诊断,验证了该算法的有效性和可行性。     

A robust DWT-based copyright verification scheme with Fuzzy ART

Protecting the intellectual property rights (IPR) of digital media is important because the illegal reproduction and modification of digital media has become increasingly serious. A robust DWT-based copyright verification scheme with Fuzzy ART that does not require the original image for ownership verification is proposed in this paper. The proposed scheme, which combines DWT, Fuzzy ART, and the quantization process, converts an image into a short robust table with the embedded ownership information. Unlike general classification, such as k-mean and fuzzy c-means, the number of clusters can be adaptively decided by the vigilance parameter of Fuzzy ART. Experimental results demonstrate that the proposed scheme is robust against common image processing, geometric distortions, and intentional attacks. The original image is not required to extract the embedded ownership image.     

基于ART2的Q学习算法研究

为了解决Q学习应用于连续状态空间的智能系统所面临的"维数灾难"问题,提出一种基于ART2的Q学习算法.通过引入ART2神经网络,让Q学习Agent针对任务学习一个适当的增量式的状态空间模式聚类,使Agent无需任何先验知识,即可在未知环境中进行行为决策和状态空间模式聚类两层在线学习,通过与环境交互来不断改进控制策略,从而提高学习精度.仿真实验表明,使用ARTQL算法的移动机器人能通过与环境交互学习来不断提高导航性能.     

Fuzzy rule extraction by bacterial memetic algorithms

In our previous papers, fuzzy model identification methods were discussed. The bacterial evolutionary algorithm for extracting fuzzy rule base from a training set was presented. The Levenberg–Marquardt method was also proposed for determining membership functions in fuzzy systems. The combination of the evolutionary and the gradient‐based learning techniques is usually called memetic algorithm. In this paper, a new kind of memetic algorithm, the bacterial memetic algorithm, is introduced for fuzzy rule extraction. The paper presents how the bacterial evolutionary algorithm can be improved with the Levenberg–Marquardt technique. © 2009 Wiley Periodicals, Inc.     

Feature recognition using ART2: a self-organizing neural network

A self-organizing neural network, ART2, based on adaptive resonance theory (ART), is applied to the problem of feature recognition from a boundary representation (B-rep) solid model. A modified face score vector calculation scheme is adopted to represent the features by continuous-valued vectors, suitable to be input to the network. The face score is a measure of the face complexity based upon the convexity or concavity of the surrounding region. The face score vector depicts the topological relations between a face and its neighbouring faces. The ART2 network clusters similar features together. The similarity of the features within a cluster is controlled by a vigilance parameter. A new feature presented to the net is associated with one of the existing clusters, if the feature is similar to the members of the cluster. Otherwise, the net creates a new cluster. An algorithm of the ART2 network is implemented and tested with nine different features. The results obtained indicate that the network has significant potential for application to the problem of feature recognition.     

Intelligent forecasting system based on integration of electromagnetism-like mechanism and fuzzy neural network

Fuzzy neural network (FNN) architectures, in which fuzzy logic and artificial neural networks are integrated, have been proposed by many researchers. In addition to developing the architecture for the FNN models, evolution of the learning algorithms for the connection weights is also a very important. Researchers have proposed gradient descent methods such as the back propagation algorithm and evolution methods such as genetic algorithms (GA) for training FNN connection weights. In this paper, we integrate a new meta-heuristic algorithm, the electromagnetism-like mechanism (EM), into the FNN training process. The EM algorithm utilizes an attraction–repulsion mechanism to move the sample points towards the optimum. However, due to the characteristics of the repulsion mechanism, the EM algorithm does not settle easily into the local optimum. We use EM to develop an EM-based FNN (the EM-initialized FNN) model with fuzzy connection weights. Further, the EM-initialized FNN model is used to train fuzzy if–then rules for learning expert knowledge. The results of comparisons done of the performance of our EM-initialized FNN model to conventional FNN models and GA-initialized FNN models proposed by other researchers indicate that the performance of our EM-initialized FNN model is better than that of the other FNN models. In addition, our use of a fuzzy ranking method to eliminate redundant fuzzy connection weights in our FNN architecture results in improved performance over other FNN models.     

Application of a fuzzy integral for weak classifiers boosting

A new multiclassifier algorithm, called FuzzyBoost, is proposed. FuzzyBoost provides nonlinear composition model construction and is based on the well-known AdaBoost algorithm, but with additional steps for estimation of fuzzy densities of weak classifiers and calculation of the fuzzy integral instead of the AdaBoost linear aggregation rule at each step of boosting. Experimental studies demonstrated that Fuzzy-Boost has better generalization ability than AdaBoost in the cases of small-size training sets and small-size feature space with correlated features.     

2-D Shape Recognition using Recursive Landmark Determination and Fuzzy ART Network Learning

In this Letter, 2-D shape recognition is done using a combination of recursive search of landmarks, landmark-based invariant features, and a fuzzy ART neural-network classifier. To make this novel combination work well, an upper limit is imposed on the number of total landmarks allowed, and this maximum size is then translated into fixed dimensions of invariant features and into the neural processing of the features. It is shown that the recursive landmark search approximates very well any smooth 2-D shape contour, that the shape features used are independent of perspective transformation, and that, when combinedwitha fuzzy ART classifier, unknown features can be efficiently learned on-line to identify multiple distinct objects. An illustrative example is used to demonstrate effectiveness of the proposed algorithm.     

A “mutual update” training algorithm for fuzzy adaptivelogic control/decision network (FALCON)

The conventional two-stage training algorithm of the fuzzy/neural architecture, called FALCON, may not provide accurate results for certain type of problems, due to the implicit assumption of independence that this training makes about parameters of the underlying fuzzy inference system. In this paper, a training scheme is proposed for this fuzzy/neural architecture, which is based on line search methods that have long been used in iterative optimization problems. This scheme involves synchronous update of the parameters of the architecture corresponding to input and output space partitions and rules defining the underlying mapping; the magnitude and direction of the update at each iteration is determined using the Armijo rule. In our motor fault detection study case, the mutual update algorithm arrived at the steady-state error of the conventional FALCON training algorithm is twice as fast and produced a lower steady-state error by an order of magnitude.     

使用LSA降维的改进ART2神经网络文本聚类

针对文本数据高维度的特点和聚类的动态性要求,结合隐含语义分析（LSA）降维,提出一种改进的ART2神经网络文本聚类算法,通过LSA凸显文本和词条之间的语义关系,减少无用噪声,降低数据维度和计算复杂性;采用改进的折中学习方法,减少计算步骤,加快ART2神经网络计算速度,并利用最近邻动态重组方法提高ART2网络聚类的稳定性,减弱算法对样本输入顺序的依赖。实验表明,改进的文本聚类算法能有效地实现动态文本聚类。     

基于BP神经网络的模糊参数辨识

提出一种与TSK模糊模型相似的模糊模型—M-2模型,证明了M-2模型与一个4层前向神经网络是等价的,在此基础上提出基于BP神经网络的模糊模型参数辨别算法,即通过BP神经网络对样本数据的学习,直接从样本数据获取模型参数,建立M-2模糊模型,通过仿真实例验证了该算法的有效性。     

Parallel implementation of the fuzzy ARTMAP neural network paradigm on a hypercube

Abstract: The recent surge of interest in connectionist models arose through the availability of high speed parallel supercomputers and the advent of new learning algorithms. The computations performed on concurrent architectures are less costly than similar ones performed on sequential machines. In this paper, the design and implementation of a parallel version of fuzzy ARTMAP (Carpenter et al. 1992), which encompasses both neural and fuzzy logic, is discussed. Fuzzy ARTMAP is a supervised learning algorithm utilising two fuzzy ART modules and an associated mapping network. A simplified version of fuzzy ARTMAP (SFAM) was designed by incorporating a simplification of the match tracking concept on unsupervised fuzzy ART paradigms. The proposed simplified version consists of only one fuzzy ART module and an associated mapping network. A parallel fuzzy ARTMAP (PFAM) algorithm is then designed and implemented on a hypercube simulator (iPSC). The algorithm is parallelised for any architecture and, with the exception of issues related to communications, the implementation remains the same on any type of parallel machine. PFAM enjoys the advantage of reduced training time that makes the algorithm a successful candidate for applications that require both online testing and training. Such applications can range from underwater sonar detection and chemical plant processing control to nuclear reactor process control, flexible manufacturing and systems analysis.