模糊数学在预测溶解法采盐地面下沉中的应用

通过建立某一地面下沉值与影响下沉的诸因素间的模糊关系方程,找出各指标间的模糊关系并进行求解;然后将要预计的厚覆盖岩层采区地面下沉值的各有关影响因素指标之实测值,通过实际求得的模糊关系作模糊变换,从而可得出采区下沉的最佳预测值。通过工程实例分析对比结果表明,理论分析结果与实测资料吻合。     

sup-conjunctor合成模糊关系方程有惟一极小解的条件

讨论[0,1]格上sup-conjunctor合成模糊关系方程有惟一极小解与惟一解的问题。首先给出了sup-conjunctor合成模糊关系方程解集的性质及两个算子IT与LT.然后定义并给出了sup-conjunctor合成模糊关系方程的特征矩阵。最后通过特征矩阵给出sup-conjunctor合成模糊关系方程有惟一极小解与惟一解的充要条件。     

求解具有max-t-norm合成算子的模糊关系不等式

研究一类具有max-t-norm合成算子的模糊关系不等式,该问题是模糊关系方程以及区间值模糊关系方程的推广.通过分析其解集的特点,提出一个基于筛选原则求解该类问题的算法,并给出数值算例说明该算法的有效性.     

活动区特征的模糊统计与综合评估研究

运用模糊统计与综合评估的方法,探索日面活动区的活动性与活动区的光学和射电辐射特征间的联系, 根据它们间的关系,构造隶属函数,综合评估和预测活动区的活动性.通过数据处理与分析,获得了活动区活动性预测的、较准确的结果,预测的准确性>95%.由此可见,模糊预测方法是一种较好的预报方法.     

模糊Logistic方程的稳定性

研究了具有广义Hukuhara导数的模糊Logistic方程,将模糊Logistic方程转化成两个常微分方程,分析了模糊Logistic方程平衡点的稳定性.结论表明,模糊Logistic方程平衡点的稳定性不同于确定的Logistic方程,丰富了模糊微分方程研究的内容.     

加型一致性毕达哥拉斯模糊偏好关系

在模糊偏好关系两种等价的加型一致性概念基础上,通过简单的数学证明,分析了区间值模糊偏好关系、直觉模糊偏好关系的相应的两种加型一致性并不是等价的.然后,在加型一致性直觉模糊偏好关系的启发下,构造了可以与毕达哥拉斯模糊偏好关系相互转换的两个区间值模糊偏好关系,并利用它们的加型一致性,定义了加型一致性毕达哥拉斯模糊偏好关系,并分析了其与杨艺等定义的加型一致性毕达哥拉斯模糊偏好关系的关系.其次,研究了加型一致性毕达哥拉斯模糊偏好关系的性质以及毕达哥拉斯模糊偏好关系的满意一致性,并给出满意一致性毕达哥拉斯模糊偏好关系下的方案优劣排序算法.最后,通过两个计算实例说明了排序算法可行有效.     

区间值max-*模糊关系方程的完全解

给出了区间值max-*模糊关系方程最大解的形式;存在极小解的充要条件,进一步刻画了极小解的形式和极小解的个数,最后刻画了区间值max-*模糊关系方程的解集.     

一阶多参数模糊微分方程及定解问题

在单参数模糊微分方程基础上研究了一阶多参数模糊微分方程和模糊初值问题,利用刻画方程的解与刻画参数的关系给出了多参数模糊微分方程解存在的条件,最后给出了具体算例.表明,多参数模糊微分方程具有广泛的工程应用背景.     

模糊关系方程

<正> 模糊关系方程是模糊数学的一个基本课题,近年来,国内外在这方面开展了许多研究工作,但至今还有不少问题没有得到比较理想的结果.本文讨论了一般模糊关系方程的求解问题,对一类最常见的模糊关系方程从理论上弄清了它的解集结构并给出了一种实际解法,从而使问题得到比较彻底的解决.     

模糊随机有限元平衡方程的摄动解法*

对模糊随机有限元平衡方程作λ水平截集,得随机区间平衡方程,然后基于平衡方程中有关力学量之间的关系,将随机区间平衡方程转化为两类普通随机平衡方程求解,利用小参数摄动理论导得求随机区间位移的递归方程组.文中还详细推导了计算模糊随机位移、模糊随机应变和模糊随机应力数字特征的计算公式.     

模糊测度在山区开挖岩体移动分析中的应用

将山区地下开挖所引起的上覆岩土体的移动视为一模糊事件,应用模糊数学中的模糊测度理论,推导了相应的地表下沉及水平移动的理论计算公式,对煤矿开采所引起的地表移动进行了计算分析。通过工程实例计算分析表明,理论计算结果与工程实测资料吻合的很好。     

基于多层次模糊关联算法的通用航空产业知识挖掘研究

为挖掘通用航空产业领域知识的类型与发展规律,明确产业规划、推动产业健康发展,利用采集的3869份通用航空产业主题相关的网页资料,根据主题信息的关键词对通用航空产业领域知识进行分类,并基于多层次模糊关联算法进行知识挖掘分析.结果表明,通用航空产业领域知识中的通用航空产业主体与通用航空产业产品存在着紧密的联系和规则,而且采用的多层次模糊关联算法比精确的关联规则算法更容易操作,结果更容易理解、更容易被应用于指导通用航空产业领域的发展.     

Ground movement analysis in deep iron mine using fuzzy probability theory

The analysis of the ground movements due to underground mining operation is one of the many important problems of rock mass mechanics. It is difficult to calculate the ground movement due to deep underground mining of iron ore accurately because of the complexity of the problems. In this paper, the application is described of the fuzzy probability measures to the analysis of ground movements. Based on the definition of the fuzzy probability measure, the theories for the two- and three-dimensional problems are developed and are applied to the analysis of ground movements duo to underground deep underground mining of iron ore.     

基于模糊灰色新陈代谢FGM(1,1)模型预测及应用探讨

巷道围岩变形是煤矿开采普遍存在的问题,巷道开挖过程中围岩会出现破裂现象.主要探讨模糊灰色系统的深部巷道围岩变形预测模型及应用,通过分析模糊数学理论、灰色理论,给出模糊灰色系统预测新陈代谢模型.     

具有模糊属性的关联产生式发掘算法及应用

本文通过不确定性推理的分析，提出了模糊关联的概念，用模糊概念表示事务数据之间的关联关系，研究了模糊关联的性质，给出了模糊关联产生式的发掘算法及应用的实例．     

高陡山区岩体移动分析的Fuzzy数学模型

本文利用 Fuzzy数学理论 ,针对高陡山区地下房柱法采矿所引起的岩体移动变形问题 ,建立了具体的数学模型和预测分析方法 .通过对工程实例的计算分析 ,表明本文所建立的数学模型和分析方法适用于工程实际     

坚硬顶板爆破切顶在沿空留巷中的应用研究

当煤层上方为坚硬顶板时,在工作面回采期间容易出现大面积悬顶,导致巷旁充填体出现大变形破坏,极大威胁采煤安全.为解决这一问题,以某矿1205工作面沿空留巷为工程背景,分析了爆炸围岩裂隙范围和坚硬顶板破断特征,建立了切顶卸压围岩结构力学模型,研究表明,相邻炮孔爆炸后,炮孔间裂隙互相贯穿,降低了顶板强度,同时当切顶角为15°时,巷道稳定性较好.工程实践结果表明:采用爆破切顶技术后,顶板下沉量减小了62.3%,较好地保证了围岩稳定性.     

Developing an efficient knowledge discovering model for mining fuzzy multi-level sequential patterns in sequence databases

Sequential pattern mining from sequence databases has been recognized as an important data mining problem with various applications. Items in a sequence database can be organized into a concept hierarchy according to taxonomy. Based on the hierarchy, sequential patterns can be found not only at the leaf nodes (individual items) of the hierarchy, but also at higher levels of the hierarchy; this is called multiple-level sequential pattern mining. In previous research, taxonomies based on crisp relationships between any two disjointed levels, however, cannot handle the uncertainties and fuzziness in real life. For example, Tomatoes could be classified into the Fruit category, but could be also regarded as the Vegetable category. To deal with the fuzzy nature of taxonomy, Chen and Huang developed a novel knowledge discovering model to mine fuzzy multi-level sequential patterns, where the relationships from one level to another can be represented by a value between 0 and 1. In their work, a generalized sequential patterns (GSP)-like algorithm was developed to find fuzzy multi-level sequential patterns. This algorithm, however, faces a difficult problem since the mining process may have to generate and examine a huge set of combinatorial subsequences and requires multiple scans of the database. In this paper, we propose a new efficient algorithm to mine this type of pattern based on the divide-and-conquer strategy. In addition, another efficient algorithm is developed to discover fuzzy cross-level sequential patterns. Since the proposed algorithm greatly reduces the candidate subsequence generation efforts, the performance is improved significantly. Experiments show that the proposed algorithm is much more efficient and scalable than the previous one. In mining real-life databases, our works enhance the model's practicability and could promote more applications in business.     

Attribute reduction based on generalized fuzzy evidence theory in fuzzy decision systems

Attribute reduction is viewed as an important issue in data mining and knowledge representation. This paper studies attribute reduction in fuzzy decision systems based on generalized fuzzy evidence theory. The definitions of several kinds of attribute reducts are introduced. The relationships among these reducts are then investigated. In a fuzzy decision system, it is proved that the concepts of fuzzy positive region reduct, lower approximation reduct and generalized fuzzy belief reduct are all equivalent, the concepts of fuzzy upper approximation reduct and generalized fuzzy plausibility reduct are equivalent, and a generalized fuzzy plausibility consistent set must be a generalized fuzzy belief consistent set. In a consistent fuzzy decision system, an attribute set is a generalized fuzzy belief reduct if and only if it is a generalized fuzzy plausibility reduct. But in an inconsistent fuzzy decision system, a generalized fuzzy belief reduct is not a generalized fuzzy plausibility reduct in general.