基于直觉模糊S-粗集副集的目标合群算法

阐述直觉模糊S-粗集和直觉模糊S-粗集副集的数学结构与特性。针对空间群的动态性,用一个双向直觉模糊S-集合表示一个空间群,群中的动态目标构成直觉模糊S-粗集的副集。给出基于直觉模糊S-粗集副集的目标合群算法,对空间群中发现新目标、目标合群等动态操作过程进行分析。通过实例证明该方法的正确性和有效性,计算结果表明该方法能较灵活地处理目标编群中的动态问题。     

基于直觉模糊等价相异矩阵的聚类方法

针对直觉模糊集合数据的聚类问题,提出了一种基于直觉模糊等价相异矩阵的聚类方法。该方法首先给出直觉模糊相异区间的概念,并构建了直觉模糊相异矩阵;然后定义了直觉模糊等价相异矩阵和(α,β)截矩阵,进而给出直觉模糊聚类算法;最后将其应用于目标编群领域,通过实例验证该算法的有效性。     

ART神经网络的发展与应用

作为解决神经网络学习中“稳定性/可塑性两难问题“的一种尝试,ART神经网络一直备受关注.从最初的仅仅用于处理二值输入的非监督学习网络ART1,到具有有监督学习能力的ARTMAP网络,具有一定模糊逻辑运算能力的Fuzzy ART网络,再到现在对于ART网络中的各种尝试,ART神经网络不断发展、改进,以便适应不同的应用场合.本文着重介绍了ART网络的基本体系结构与发展历程,对于其应用领域加以概述.     

基于Fuzzy ART神经网络的Linux进程行为异常检测

研究了Linux进程行为的模式提取与异常检测问题。介绍了一种模糊神经网络Fuzzy ART及其实现,利用Fuzzy ART网络对Linux进程的系统调用序列进行模式提取,并据此进行异常检测。实验结果初步表明该方法是可行、有效的。最后说明了该方法的优点和不足。     

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.     

直觉模糊集合数据的聚类有效性分析

针对直觉模糊集合数据的聚类有效性问题,给出了一种用于发现最优模糊划分的聚类有效性方法.该方法采用直觉模糊相关度和直觉模糊熵两个重要因子来评价直觉模糊聚类的有效性.其中,直觉模糊相关度通过增加非隶属度参数对模糊相关度进行直觉化扩展,用于评价类与类间相关度的大小,同时加入权重参数解决了样本数据各维特征分配不均匀的问题,而直觉模糊熵用于检验分类结果的可靠性.最后通过实例验证了该方法对于紧致的、良好分离的教据集分类效果理想,其在目标编群、目标识别等信息融合领域有良好的应用前景.     

基于直觉模糊神经网络的机动事件检测方法

战场事件检测在态势评估的各个推理层次上都起着重要作用,是态势评估的基础.已有文献对简单的战场事件提出了相应检测方法,但并不能满足对复杂多变的现代战场进行态势评估的需求.针对战场事件类型的多样性和发生的频繁性,设计了战场事件体系结构,定义了防空作战中空中目标的战术机动事件,并设计了直觉模糊神经网络对战术机动事件进行检测.仿真实验结果表明了该检测方法的可行性和有效性.     

基于改进微粒群算法的直觉模糊整数规划

提出了一种基于改进微粒群算法的直觉模糊整数规划。首先定义了目标函数和约束函数的隶属和非隶属函数,通过直觉模糊“最小-最大”算子,提出了直觉模糊整数规划模型;然后通过对微粒群算法进行改进,对直觉模糊整数规划进行了求解,并通过一个算例表明本文的算法性能优于其他几种算法。     

粒子群优化神经网络的交通事件检测算法研究

为减少交通事件引起的交通延误,提出一种基于粒子群优化神经网络的交通事件检测算法;首先,利用车载激光测距仪和GPS设备作为实验平台,采集了反映路段车辆占有率及车辆运行速度特征的交通参数;其次,利用粒子群(PSO)算法训练随机产生的初始化数据,优化BP神经网络连接权值和阈值;最后,将PSO优化后的BP神经网络作为分类器进行交通事件的自动分类和检测;试验中比较了PSO神经网络算法、BP神经网络算法和经典算法对交通事件的检测效果,PSO神经网络算法在事件检测率(DR)、平均检测时间(MTTD)方面均优于其他目标算法;结果显示,粒子群优化的神经网络算法用于交通事件检测提高了检测性能。     

基于模糊ART神经网络的在线人脸识别模型的设计和实现

本文描述了模糊ART神经网络的结构和特性,定义了相似函数和匹配搜索方法,通过去噪、去最小亮度和设计编码簿的方法产生人脸的特征向量图,以提取人脸特征,并用模糊ART神经网络对特征向量图进行识别.仿真实验证明,当选择合适的模糊ART神经网络参数后,该模型的在线最大识别率可以达到81.25%,离线识别率几乎为100%.     

基于自适应直觉模糊推理的数据挖掘方法

针对数据挖掘问题,将直觉模糊集与神经网络理论相结合,提出一种新的方法。用自适应直觉模糊推理的方法来解决数据挖掘问题,该方法可以根据直觉模糊神经网络本身的自适应学习能力来调节网络参数,自动生成规则库。最后通过一个仿真实例证明了该方法的有效性。     

模糊ART神经网络模型及其在图像识别中的应用

提出了采用低通过率波、去最小亮度和向量柱状图来提取人脸特征的方法,设计了模糊ART神经网络的结构、学习规则和识别算法,并采用模糊ART神经网络对向量柱状图生成的特征向量进行识别。仿真实验证明,通过调整神经网络的警戒参数值,不同的人具有不同的最大在线识别率,所有人平均的在线最大识别率可以达到89％。     

基于直觉模糊——神经网络的色情图像识别算法

网络中色情图像的传播严重影响了网络信息内容的安全性。为提高色情图像识别的准确度,提出了一种直觉模糊理论和FP（Forward Propagation）神经网络相结合的色情图像识别算法。算法以颜色直方图为底层特征,根据色情图像颜色分布情况,由模糊理论和直觉模糊理论共同构建图像特征矩阵;采用FP网络实现色情图像特征训练过程,其中特征矩阵的权重通过反向传播神经网络训练得到,以加权距离建立球形邻域半径;最后通过球形邻域覆盖情况识别色情图像。实验结果表明,该算法能够在不影响识别速率的前提下,有效的提高识别准确度。     

An efficient neural classification chain of SAR and optical urban images

In this paper a suitable neural classification algorithm, based on the use of Adaptive Resonance Theory (ART) networks, is applied to the fusion and classification of optical and SAR urban images. ART networks provide a flexible tool for classification, but are ruled by a large number of parameters. Therefore, the simplified ART2-A algorithm is used in this paper, and the neural approach is integrated into a classification chain where fuzzy clustering for merging of classes is also considered. The interaction between the two methods leads to encouraging results in less CPU time than classification with fuzzy clustering alone or other classical approaches (ISODATA). Examples of classification are provided using C-band total power AIRSAR data and optical images of Santa Monica, Los Angeles.     

Falcon: neural fuzzy control and decision systems using FKP and PFKP clustering algorithms.

Neural fuzzy networks proposed in the literature can be broadly classified into two groups. The first group is essentially fuzzy systems with self-tuning capabilities and requires an initial rule base to be specified prior to training. The second group of neural fuzzy networks, on the other hand, is able to automatically formulate the fuzzy rules from the numerical training data. Examples are the Falcon-ART, and the POPFNN family of networks. A cluster analysis is first performed on the training data and the fuzzy rules are subsequently derived through the proper connections of these computed clusters. This correspondence proposes two new networks: Falcon-FKP and Falcon-PFKP. They are extensions of the Falcon-ART network, and aimed to overcome the shortcomings faced by the Falcon-ART network itself, i.e., poor classification ability when the classes of input data are very similar to each other, termination of training cycle depends heavily on a preset error parameter, the fuzzy rule base of the Falcon-ART network may not be consistent Nauck, there is no control over the number of fuzzy rules generated, and learning efficiency may deteriorate by using complementarily coded training data. These deficiencies are essentially inherent to the fuzzy ART, clustering technique employed by the Falcon-ART network. Hence, two clustering techniques--Fuzzy Kohonen Partitioning (FKP) and its pseudo variant PFKP, are synthesized with the basic Falcon structure to compute the fuzzy sets and to automatically derive the fuzzy rules from the training data. The resultant neural fuzzy networks are Falcon-FKP and Falcon-PFKP, respectively. These two proposed networks have a lean and efficient training algorithm and consistent fuzzy rule bases. Extensive simulations are conducted using the two networks and their performances are encouraging when benchmarked against other neural and neural fuzzy systems.     

一种ART2神经网络的改进算法

传统的ART2神经网络由于预处理阶段的归一化，易将重要但幅值较小的分量作为噪声清除，造成在分类中丢失重要信息，同时还存在模式漂移的不足，分析产生这些不足的原因，并基于去单位化以及类内样本与类中心的距离不同而对类中心偏移产生不同影响的思想，对传统的ART2神经网络算法进行了改进。对一组渐变数据的测试表明，改进后的网络有效改善了模式漂移现象。同时，改进的ART2神经网络在核辐射场数据处理分类中有一定的实用价值。     

Fuzzy lattice neural network (FLNN): a hybrid model for learning

This paper proposes two hierarchical schemes for learning, one for clustering and the other for classification problems. Both schemes can be implemented on a fuzzy lattice neural network (FLNN) architecture, to be introduced herein. The corresponding two learning models draw on adaptive resonance theory (ART) and min-max neurocomputing principles but their application domain is a mathematical lattice. Therefore they can handle more general types of data in addition to N-dimensional vectors. The FLNN neural model stems from a cross-fertilization of lattice theory and fuzzy set theory. Hence a novel theoretical foundation is introduced in this paper, that is the framework of fuzzy lattices or FL-framework, based on the concepts fuzzy lattice and inclusion measure. Sufficient conditions for the existence of an inclusion measure in a mathematical lattice are shown. The performance of the two FLNN schemes, that is for clustering and for classification, compares quite well with other methods and it is demonstrated by examples on various data sets including several benchmark data sets.     

An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction

The prediction of time series has both the theoretical value and practical significance in reality. However, since the high nonlinear and noises in the time series, it is still an open problem to tackle with the uncertainties and fuzziness in the forecasting process. In this article, an evolving recurrent interval type-2 intuitionistic fuzzy neural network (eRIT2IFNN) is proposed for time series prediction and regression problems. The eRIT2IFNN employs interval type-2 intuitionistic fuzzy sets to enhance the modeling of uncertainties by intuitionistic evaluation and noise tolerance of the system. In the eRIT2IFNN, the antecedent part of each fuzzy rule is defined using intuitionistic interval type-2 fuzzy sets, and the consequent realizes the Takagi–Sugeno–Kang type fuzzy inference mechanism. In order to utilize the prior knowledge including intuitionistic information, a local internal feedback is established by feeding the rule firing strength of each rule to itself eRIT2IFNN is fully adaptive to the evolving of sequence data by online learning of structure and parameters. A modified density-based clustering is implemented for the structure learning, where both densities and membership degrees are involved to determine the fuzzy rules. Performance of eRIT2IFNN is evaluated using a set of benchmark problems and compared with existing fuzzy inference systems. Moreover, the eRIT2IFNN is tested for identification of dynamics under both noise-free and noisy environments. Finally, a group of practical financial price-tracking problems including high-frequency data of financial future, commodity future and precious metal are used for the evaluation of the proposed inference system.