共查询到19条相似文献,搜索用时 192 毫秒
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煤矿安全预警系统体系构建 总被引:1,自引:0,他引:1
针对煤矿安全监测数据的特点,构建了基于数据挖掘与数据融合的煤矿安全预警集成系统,分析了该系统的运行流程;并综合采用FCM聚类算法、粗糙集理论和RBF神经网络,详细设计了系统中最关键的数据挖掘模块. 相似文献
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随着数据挖掘技术的兴起,为了提高数据挖掘的准确性,提出了很多数据挖掘算法.神经网络与粗糙集理论结合的数据挖掘算法一直是基于粗糙集理论数据挖掘研究的热点之一.文中提出利用RBF神经网络收敛速度快、泛化能力强等优势先对数据进行训练,优化数据后传递给粗糙集进行数据挖掘的新思路.并通过对比与未经过RBF神经网络训练的数据挖掘结果,发现RBF神经网络与粗糙集结合算法挖掘的精度有明显的提高,证明了RBF神经网络与粗糙集理论结合的数据挖掘算法是有效的、可行的. 相似文献
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基于粗糙集理论和BP神经网络的数据挖掘算法 总被引:12,自引:1,他引:11
根据数据挖掘中粗糙集理论和BP神经网络各自的优势和存在的问题,提出了一种将粗糙集理论和BP神经网络理论结合在一起的算法。该算法利用粗糙集对属性的归约功能将数据仓库中的数据进行归约,并将归约后的数据作为训练数据提供给BP神经网络。通过粗糙集归约,提高了训练数据表达的清晰度,也减小了BP神经网络的规模,同时利用BP神经网络又克服了粗糙集对噪声数据敏感的影响。文中提出了代价函数,解决了训练数据与网络精度的问题,也提供了由粗糙集归约向BP神经网络训练转变的依据。 相似文献
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基于数据挖掘的瓦斯灾害信息融合模型的研究 总被引:1,自引:0,他引:1
数据挖掘和信息融合是2种功能不同的处理数据的过程,2种方法虽然原理不同,但在功能上可以相互弥补。介绍了基于数据挖掘技术建立信息融合模型的原理和算法,研究了基于模糊粗糙集的数据挖掘算法建立瓦斯灾害信息融合模型的方法,并对所建模型进行误差曲线仿真分析。 相似文献
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介绍了数据挖掘与数据融合集成系统的理论基础,构造了一种粗糙集——模糊神经网络模型以实现该集成系统,给出了系统的原理图和流程图,并对构造模型的算法进行了研究。最后,阐述了集成系统的优势。 相似文献
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数据融合与数据挖掘相集成的自动目标识别系统 总被引:2,自引:0,他引:2
该文提出数据融合技术与数据挖掘技术相集成的海上目标自动识别系统体系结构,以及基于加权粗糙集模型的特征知识挖掘方法,并运用模糊神经网络技术进行目标识别。 相似文献
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软计算方法在数据挖掘中的应用 总被引:5,自引:0,他引:5
对数据挖掘和软计算方法做了一个综合性的阐述,分析了模糊逻辑、神经网络、神经一模糊、遗传算法、粗糙集和混合方法等不同软计算方法的特点,尤其是对模糊逻辑软计算方法进行了更多的探讨,并结合软计算方法在数据挖掘中的应用现状,指出了数据挖掘面临的挑战和软计算方法的应用前景。 相似文献
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《Neurocomputing》1999,24(1-3):37-54
This paper presents some highlights in the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition. These techniques are capable of dealing with inexact and imprecise problem domains and have been demonstrated to be useful in the solution of classification problems. It addresses the issue of the application of appropriate evaluation criteria such as rule base accuracy and comprehensibility for new knowledge acquisition techniques. An empirical study is then described in which three approaches to knowledge acquisition are investigated. The first approach combines neural networks and fuzzy logic, the second, genetic algorithms and fuzzy logic, and in the third a rough sets approach has been examined, and compared. In this study neural network and genetic algorithm fuzzy rule induction systems have been developed and applied to three classification problems. Rule induction software based on rough sets theory was also used to generate and test rule bases for the same data. A comparison of these approaches with the C4.5 inductive algorithm was also carried out. Our research to date indicates that, based on the evaluation criteria used, the genetic/fuzzy approach compares more than favourably with the neuro/fuzzy and rough set approaches. On the data sets used the genetic algorithm system displays a higher accuracy of classification and rule base comprehensibility than the C4.5 inductive algorithm. 相似文献
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基于粗糙集理论的神经网络研究及应用 总被引:2,自引:0,他引:2
为了补偿神经网络的黑箱特性并提高其工作性能,将粗糙集理论同神经网络结合起来,提出一种基于粗糙集的神经网络体系结构.首先,利用粗糙集理论对神经网络初始化参数的选择和确定进行指导,赋予各参数相关的物理意义;然后,以系统输出误差最小化为目标对粗糙神经网络进行训练,使其满足性能要求.实验结果表明,粗糙神经网络能较好地完成数据挖掘任务,并能获得较高的分类精度. 相似文献
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提出了一种基于神经网络和粗集的数据挖掘新方法。首先利用粗集理论对原始数据进行一致性属性约简,然后使用神经网络对数据进行学习,并同时完成属性的不一致约简,最后再由粗集对神经网络中的知识进行规则抽取。该方法充分融合了粗集理论强大的属性约简、规则生成能力和神经网络优良的分类、容错能力。实验表明,该方法快速有效,生成规则简单准确,具有良好的鲁棒性。 相似文献
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从数据挖掘的定义出发,介绍了数据挖掘的神经网络法、决策树法、遗传算法、粗糙集法、模糊集法和关联规则法等概念及其各自的优缺点;详细总结了国内外数据挖掘的研究现状及研究热点,指出了数据挖掘的发展趋势。 相似文献
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Yasser F. Hassan 《Applied Intelligence》2011,35(2):260-268
Classification is an important theme in data mining. Rough sets and neural networks are two techniques applied to data mining
problems. Wavelet neural networks have recently attracted great interest because of their advantages over conventional neural
networks as they are universal approximations and achieve faster convergence. This paper presents a hybrid system to extract
efficiently classification rules from decision table. The neurons of such hybrid network instantiate approximate reasoning
knowledge gleaned from input data. The new model uses rough set theory to help in decreasing the computational effort needed
for building the network structure by using what is called reduct algorithm and a rules set (knowledge) is generated from
the decision table. By applying the wavelets, frequencies analysis, rough sets and dynamic scaling in connection with neural
network, novel and reliable classifier architecture is obtained and its effectiveness is verified by the experiments comparing
with traditional rough set and neural networks approaches. 相似文献
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基于粗糙集理论的数据挖掘算法及其应用研究 总被引:4,自引:0,他引:4
文章对粗糙集理论及其应用进行了讨论,在分析和综合基于粗糙集理论的数据挖掘算法基础上,提出了新的遗传算法挖掘方法,并就应用模型和应用领域及方法问题进行了分析。通过应用实例表明,文章提供的方法和技术是可行的,具有较大的参考价值。 相似文献
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随着大型数据库的不断涌现,如何从浩如烟海的数据中发现隐藏的有用知识,成为一个迫切需要研究的课题.因此,知识发现和数据挖掘应运而生.该文提出了数据挖掘的基本概念,数据挖掘是数据库技术、人工智能、机器学习、统计分析、模糊逻辑、模式识别和人工神经网络等多个学科相结合的产物,然后分析了数据挖掘一般算法的结构,并且对数据挖掘技术进行了详细分类,主要包括决策树技术、神经网络技术、粗集以及模糊集等十多项挖掘技术.最后讨论了数据挖掘在人工智能、电子商务应用和移动通信计算等方面的研究方向. 相似文献
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Shifei Ding Hongjie Jia Jinrong Chen Fengxiang Jin 《Artificial Intelligence Review》2014,41(3):373-384
Fuzzy neural networks (FNNs) and rough neural networks (RNNs) both have been hot research topics in the artificial intelligence in recent years. The former imitates the human brain in dealing with problems, the other takes advantage of rough set theory to process questions uncertainly. The aim of FNNs and RNNs is to process the massive volume of uncertain information, which is widespread applied in our life. This article summarizes the recent research development of FNNs and RNNs (together called granular neural networks). First the fuzzy neuron and rough neuron is introduced; next FNNs are analysed in two categories: normal FNNs and fuzzy logic neural networks; then the RNNs are analysed in the following four aspects: neural networks based on using rough sets in preprocessing information, neural networks based on rough logic, neural networks based on rough neuron and neural networks based on rough-granular; then we give a flow chart of the RNNs processing questions and an application of classical neural networks based on rough sets; next this is compared with FNNs and RNNs and the way to integrate is described; finally some advice is given on development of FNNs and RNNs in future. 相似文献