CBR技术在自动配色系统中的应用研究

为提高复杂多变条件下的配色准确率,提出了基于案例推理（case-based reasoning,CBR）的新型自动配色技术,并给出了原型--基于案例推理的配色系统(CBRCMS). CBRCMS对配方库组织结构、配方检索、配方复用、学习调整等关键技术进行了探讨.CBRCMS基于大量的历史优良配方,以色度迭代逼近技术作为配色精度的基本保证,并将其作为CBR配方复用的主要方法.实验数据显示,CBR的引入可明显提高配色准确率,CBR技术的自适应、自学习特点对传统配色技术是很好的补充.     

案例推理属性权重的分配模型比较研究

案例推理系统中各属性权重的赋值决定了案例之间的相似度 大小,进而对推理结果的正确与否产生显著影响.以属性加权K-最近邻 相似案例检索为基础,讨论了使用注水原理分配属性权重的机理,并通过建 立权重分配的合理性指标,构造拉格朗日函数对权重进行优 化求解,得到了收敛的注水分配算法.通过五折交叉的模式分类实验 ,分别对属性权重的平均分配法、注水分配算法和遗传算法分配法进行了比较研究,案例推理分类结果证明,在引入注水分配算法后,其分类性能得到有效改善.     

基于TOPSIS-模糊综合评判的模糊推理模型在开河预报中的应用

冰凌开封河受到较多自然和人为因素的影响,具有较高的不确定性,为了进一步提高冰凌开封河预测的精度,考虑各因素的综合作用成为解决问题的关键。先采用主成分分析法初步确定冰凌开封河历时影响因子的权重,运用模糊推理模型依据影响因子矩阵的相似性进行初步预测,进而采用TOPSIS-模糊综合评判模型对预报因子进行识别,筛选出合理的预报因子进行二次预测。运用实例对基于TOPSIS-模糊综合评判模型冰凌预报因子识别的模糊推理模型的效果进行了检验,同时与冰凌预报模糊优选神经网络BP模型进行对比,结果表明:在TOPSIS-模糊综合评判模型因子进行识别基础上的模糊推理模型预测精度较高、效果较好,既能够有效识别预报因子,又能够较好地提高预报封河、开河历时的精度,为凌汛预测提供了新的途径。     

二维参数化图形技术研究进展

近几年来，参数化图形技术得到了不断的发展。目前已有多种参数化图形方法及系统。这些参数化图形方法可分为３种，即变分几何法、几何推理法和过程法。主要介绍这３种方法的研究进展。     

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.     

基于本体的语义虚拟环境查询与推理模型

当前将本体引入到语义虚拟环境的研究,只是将领域本体的可视化信息用本体表示,并未发挥本体本身具有的优势。为此,提出一种基于本体的语义虚拟环境查询与推理模型。利用OWL语言统一描述虚拟场景图形内容与语义信息,并分别对两者进行查询,在图形内容查询过程中引入本体的推理方法推理出隐含的图形内容信息,然后查询需要的信息。在语义信息查询时引入语义搜索方法,利用基于语义距离计算本体概念相似度的方法计算语义虚拟环境本体中类之间的相似度,搜索与被查询实例语义相似度最大的实例,并借助推理找出其间的关系。对语义虚拟家具商店进行本体的查询与推理,结果证明了该模型的可行性。     

格值一阶逻辑LF(X)中的α-语义归结方法

自动推理是人工智能的一个重要研究方向,基于归结原理的自动推理因易于在计算机上实现而得到广泛研究。语义归结是对归结原理的一种改进,它利用限制参与归结子句类型和归结文字顺序的方法来提高推理效率。为了提高基于格蕴涵代数的格值逻辑的α-归结原理的效率,将语义归结策略应用于α-归结原理。首先给出了格值一阶逻辑系统中的α-语义归结概念和α-语义归结演绎概念,接着讨论了格值一阶逻辑系统的α-语义归结方法,并证明了其可靠性和条件完备性,最后通过实例说明了其有效性。     

一阶谓词逻辑的图形推理法

针对已有一阶谓词逻辑推理方法中存在的推理效率低等问题,研究一种基于谓词/变迁系统的图形推理法。定义了描述谓词间与/或关系的谓词-与/或图,借助谓词-与/或图表示谓词/变迁系统,提出一种实现反向推理的目标制导的图形推理法。该方法推理效率高,较已有的推理方法具有一定的优越性。     

语言真值直觉模糊逻辑的知识推理

针对格蕴涵代数、直觉模糊集及知识表示、基于语言真值直觉模糊代数的相关性质及运算方法,提出了六元语言真值直觉模糊代数的相关逻辑性质,并在六元语言真值直觉模糊知识表示的基础上,将模糊推理的CRI方法进行扩展,研究得出了六元语言真值直觉模糊推理的方法即6LTV-CRI算法。而后将直觉模糊推理与六元语言真值直觉模糊推理方法进行对比分析,验证了6LTV-CRI推理算法的合理性,并分析了其优缺点。     

Composing Cardinal Direction Relations Based on Interval Algebra

Direction relations between extended spatial objects are important commonsense knowledge. Skiadopoulos proposed a formal model for representing direction relations between compound regions (the finite union of simple regions), known as SK-model. It perhaps is currently one of most cognitive plausible models for qualitative direction information, and has attracted interests from artificial intelligence and geographic information system. Originating from Allen first using composition table to process time interval constraints; composing has become the key technique in qualitative spatial reasoning to check the consistency. Due to the massive number of basic directions in SK-model, its composition becomes extraordinary complex. This paper proposed a novel algorithm for the composition. Basing the concepts of smallest rectangular directions and its original directions, it transforms the composition of basic cardinal direction relations into the composition of interval relations corresponding to Allen''s interval algebra. Comparing with existing methods, this algorithm has quite good dimensional extendibility, that is, it can be easily transferred to the tridimensional space with a few modifications.