基于改进置信规则库推理的分类方法

通过引入置信规则库的线性组合方式,设定规则数等于分类数及改进个体匹配度的计算方法,提出了基于置信规则库推理的分类方法。比较传统的置信规则库推理方法,新方法中规则数的设置不依赖于问题的前件属性数量或候选值数量,仅与问题的分类数有关,保证了方法对于复杂问题的适用性。实验中,通过差分进化算法对置信规则库的规则权重、前件属性权重、属性候选值和评价等级的置信度进行参数学习,得到最优的参数组合。对3个常用的公共分类数据集进行测试,均获得理想的分类准确率,表明新分类方法合理有效。     

基于覆盖聚类的TS模糊神经网络热工对象建模

热工过程具有非常复杂的动态特性以及强耦合、大延迟和不确定等特征。控制过程需要较为精确的模型,但是常规的建模往往并不能满足要求,因此提出一种改进型的TS模糊神经网络建模方法。首先基于一种覆盖聚类算法对离线数据进行分类,初步得到模糊神经网络的前件和后件参数,再利用卡尔曼滤波算法调整后件参数和动态梯度算法调整隶属函数的宽度和中心,最后把得到的前件参数和后件参数进入在线网络,若进入网络的实时数据不属于所有的类,则应增加聚类中心和规则。     

基于协同进化算法的高维模糊分类系统的设计

基于协同进化算法，提出一种高维模糊分类系统的设计方法．首先定义系统的精确性指标，给出解释性的必要条件，利用聚类算法辨识初始模型．相互协作的3类种群分别代表系统的特征变量、规则前件和模型隶属函数的参数，适应度函数采用3类种群合作计算的策略，在算法运行中利用基于相似性的模型简化技术约简模糊系统，最后利用该方法对Wine问题进行研究．仿真结果表明该方法能够对高维分类问题的特征变量进行选择，同时利用较少规则和模糊集合数达到较高的识别率．     

面向软件模糊自适应的语音式任务目标识别与结构化转换

已有语音识别方法将用户用英文语音表达的任务目标直接施加到模糊自适应环中,采取直接将识别结果匹配规则前件的方法,限制了系统的识别能力。为此,提出一种语音式任务目标的结构化转换方法。对于语音式任务目标进行句法分析和关键成分提取,对关键成分进行语义关联拓展,建立与任务目标等价的语义关联集合,基于集合完成面向模糊规则前件的结构化转换。通过搭建任务机器人实验系统,验证了该方法具有较好的语音式任务目标识别能力。     

复杂模糊分类系统的协同进化设计方法

提出一种基于协同进化算法的复杂模糊分类系统的设计方法．该方法由以下3步组成:1)利用Simba算法进行特征变量选择;2)采用模糊聚类算法辨识初始的模糊模型;3)利用协同进化算法对所获得的初始模糊模型进行结构和参数的优化．协同进化算法由三类种群组成;规则数种群,规则前件种群和隶属函数种群;其适应度函数同时考虑模型的精确性和解释性,采用三类种群合作计算的策略．利用该方法对多个典型问题进行分类,仿真结果验证了方法的有效性．     

多分类器组合研究

文章提出了一种多分类器的组合方法,它利用了参与组合的分类器提供的度量层次上的两类信息:对训练样本的决策信息;对待识样本的决策信息。首先对这两类信息进行集成,进而给出了组合分类器的判定规则。用该方法对手写体汉字作分类识别,实验结果显示,较之其它几种方法,它有更高的正确识别率。     

基于多种群量子进化的区间二型模糊规则挖掘算法

利用智能优化算法挖掘模糊分类规则能够解决模糊前件参数和无关项的组合优化问题,但也存在依赖初始规则以及更新过程无指导等缺陷,导致分类精度难以保证.为此,本文以二型模糊规则分类系统为框架,采用模糊聚类得到代表性样本并启发式的产生初始规则,以量子等位基因形式对规则进行编码生成多初始种群,根据基因的优良性,以变尺度变异操作实现等位基因的指导性进化.在此基础上,利用矛盾规则重构机制,提高模糊规则分类系统的精度.将所提出算法与FH–GBML–IVFS–Amp算法和GAGRAD算法进行了分类精度对比,并在不同噪声水平下,与C4.5算法、朴素贝叶斯分类器和BP神经网络进行分类鲁棒性比较,实验结果表明所提出算法具有较好分类精度与鲁棒性.     

基于特征词复合权重的关联网页分类

针对网页分类中关联分类方法存在的如下两点不足:(1)仅把网页当成纯文本处理,忽略了网页的标签信息,(2)仅用网页中的特征词作为关联规则的项,没有考虑特征词的权重,或仅以词频来量化权重,忽略了特征词位置特征的影响,提出了基于特征词复合权重的关联网页分类方法。该方法利用网页标签信息所体现的位置特征计算特征词的复合权重,并以此权重为基础建立分类规则,对网页进行分类。实验结果表明,该方法取得了比传统的关联分类方法更好的效果。     

使用新混合模糊聚类算法的模糊系统建模方法

为了进一步提高模糊系统建立模型的精度,提出一种新的模糊系统算法ANFIS-HC-QPSO：采用一种混合型模糊聚类算法来对模糊系统的输入空间进行划分,每一个聚类通过高斯函数的拟合产生一个隶属度函数,即完成ANFIS系统的前件参数--隶属度函数参数的初始识别,通过具有量子行为的粒子群算法QPSO与最小二乘法优化前件参数,直至达到停机条件,最终得到ANFIS的前件及后件参数,从而得到满意的模糊系统模型。实验表明,AN-FIS-HC-QPSO算法与传统算法相比,能在只需较少模糊规则的前提下就使模糊系统达到更高的精度。     

模糊系统建模方法及其在微生物发酵中的应用

提出一种具有量子行为的模糊系统建模方法。避免事先指定聚类数目及中心,采用混合模糊聚类算法对模糊系统的输入空间进行划分,每个聚类通过高斯函数的拟合产生一个隶属度函数,完成ANFIS前件参数的初始识别;通过具有量子行为的粒子群算法与最小二乘法优化前件参数,得到ANFIS的前件及后件参数。将该方法应用于实际的抗坏血酸2-葡萄糖苷生产发酵模型的建立中,实验结果表明,该方法具有较高精度,符合实际生产需要。     

Use of multi-objective genetic rule selection for examining the effectiveness of inter-vehicle communication in traffic simulations

Recently, inter-vehicle communication (IVC) has been actively studied to attempt to avoid traffic congestion. In this article, we propose the idea of using fuzzy rules to examine the effectiveness of IVC. In the proposed approach, we first collect travel records (e.g., travel time, travel path, traffic volume) of vehicles with IVC from our cellular automata-based traffic simulator. Various kinds of available information for vehicles with IVC are used in the antecedent part of our fuzzy rules. The level of effectiveness of IVC is discretized into four categories (i.e., four classes) in this article. The consequent class of each fuzzy rule is one of those four classes. Next we generate a large number of fuzzy rules from the collected data. Then we select only a small number of fuzzy rules by multi-objective genetic rule selection. We use three objectives: to maximize the accuracy, to minimize the number of selected rules, and to minimize the total rule length (i.e., the total number of antecedent conditions). Our approach can find a number of nondominated fuzzy-rule-based systems with respect to their accuracy and complexity. Finally, we analyze the effectiveness of IVC using fuzzy rules in the fuzzy-rule-based systems obtained through their linguistic interpretation.     

A multilevel weighted fuzzy reasoning algorithm for expert systems

The applications of fuzzy production rules (FPR) are rather limited if the relative degree of importance of each proposition in the antecedent contributing to the consequent (i.e., the weight) is ignored or assumed to be equal. Unfortunately, this is the case for many existing FPR and most existing fuzzy expert system development shells or environments offer no such functionality for users to incorporate different weights in the antecedent of FPR. This paper proposes to assign a weight parameter to each proposition in the antecedent of a FPR and a new fuzzy production rule evaluation method (FPREM) which generalizes the traditional method by taking the weight factors into consideration is devised. Furthermore, a multilevel weighted fuzzy reasoning algorithm (MLWFRA) incorporating this new FPREM, which is based on the reachability and adjacent place characteristics of a fuzzy Petri net, is developed. The MLWFRA has the advantages that i) it offers multilevel reasoning capability; ii) it allows multiple conclusions to be drawn if they exist; iii) it offers a new fuzzy production rule evaluation method; and iv) it is capable of detecting cycle rules     

Performance evaluation of fuzzy rule-based classification systems obtained by multi-objective genetic algorithms

In this paper, we examine the classification performance of fuzzy if-then rules selected by a GA-based multi-objective rule selection method. This rule selection method can be applied to high-dimensional pattern classification problems with many continuous attributes by restricting the number of antecedent conditions of each candidate fuzzy if-then rule. As candidate rules, we only use fuzzy if-then rules with a small number of antecedent conditions. Thus it is easy for human users to understand each rule selected by our method. Our rule selection method has two objectives: to minimize the number of selected fuzzy if-then rules and to maximize the number of correctly classified patterns. In our multi-objective fuzzy rule selection problem, there exist several solutions (i.e., several rule sets) called “non-dominated solutions” because two conflicting objectives are considered. In this paper, we examine the performance of our GA-based rule selection method by computer simulations on a real-world pattern classification problem with many continuous attributes. First we examine the classification performance of our method for training patterns by computer simulations. Next we examine the generalization ability for test patterns. We show that a fuzzy rule-based classification system with an appropriate number of rules has high generalization ability.     

基于置信规则库推理的二择众仓分类方法

针对线性组合方式所构建的置信规则库存在常常无法准确发挥前件属性权重的效能,且随着评价等级个数的增加,新激活权重公式往往会对结果造成不利影响的不足,本文在现有置信规则库推理分类算法的基础上,提出二择众仓决策法,以此改进置信规则库决策系统。首先仅设置两个规则的后件评价等级,对一个决策问题仅做出二择判定,即回答是与否;其次,设置多个置信规则库同时处理若干个子问题;最后通过众仓决策方式融合多个子问题的结果,进而解决最终的分类问题。实验结果表明,改进后的置信规则库推理分类方法可行有效。     

挖掘正相关的频繁项集

在由频繁项集产生关联规则时，利用提升度判断规则前、后件之间的正相关性可以避免产生一些无意义的关联。但是，这并不能保证规则前、后件中的项是正相关的，也不能减少挖掘频繁项集的时间开销。当规则的前件或后件存在负相关的项时，仍然可能产生无意义的关联规则。针对以上问题，基于数学期望，提出了正相关的频繁项集的概念，并改进了一种直接在FP-树中挖掘频繁项集的算法，挖掘出正相关的频繁项集，从而有效地解决以上问题。实验表明，该算法可以大幅度地减少所产生的频繁项集数量，显著地降低了挖掘频繁项集的时间开销。对于大型数据集，尤其是稠密型数据集，该算法具有良好的性能。     

Representing membership functions as points in high-dimensionalspaces for fuzzy interpolation and extrapolation

This paper introduces a new approach for fuzzy interpolation and extrapolation of sparse rule base comprising of membership functions with finite number of characteristic points. The approach calls for representing membership functions as points in high-dimensional Cartesian spaces using the locations of their characteristic points as coordinates. Hence, a fuzzy rule base can be viewed as a set of mappings between the antecedent and consequent spaces and the interpolation and extrapolation problem becomes searching for an image in the consequent space upon given an antecedent observation. The present approach divides observations into two groups: 1) observations within the antecedent spanning set contain the same geometric properties as the given antecedents; and 2) observations lying outside the antecedent spanning set contain new geometric properties beyond those of the given rules. Heuristic reasoning must therefore be applied. In this case, a two-step approach with certain flexibility to accommodate additional criteria and design objectives is formulated     

A comparative study on similarity-based fuzzy reasoning methods

If the given fact for an antecedent in a fuzzy production rule (FPR) does not match exactly with the antecedent of the rule, the consequent can still be drawn by technique such as fuzzy reasoning. Many existing fuzzy reasoning methods are based on Zadeh's Compositional Rule of Inference (CRI) which requires setting up a fuzzy relation between the antecedent and the consequent part. There are some other fuzzy reasoning methods which do not use Zadeh's CRI. Among them, the similarity-based fuzzy reasoning methods, which make use of the degree of similarity between a given fact and the antecedent of the rule to draw the conclusion, are well known. In this paper, six similarity-based fuzzy reasoning methods are compared and analyzed. Two of them are newly proposed by the authors. The comparisons are two-fold. One is to compare the six reasoning methods in drawing appropriate conclusions for a given set of FPRs. The other is to compare them based on five issues: 1) types of FPR handled by these methods; 2) the complexity of the methods; 3) the accuracy of the conclusion drawn; 4) the accuracy of the similarity measure; and 5) the multi-level reasoning capability. The results have shed some lights on how to select an appropriate fuzzy reasoning method under different environments.     

A correlation-based ant miner for classification rule discovery

In recent years, a few sequential covering algorithms for classification rule discovery based on the ant colony optimization meta-heuristic (ACO) have been proposed. This paper proposes a new ACO-based classification algorithm called AntMiner-C. Its main feature is a heuristic function based on the correlation among the attributes. Other highlights include the manner in which class labels are assigned to the rules prior to their discovery, a strategy for dynamically stopping the addition of terms in a rule’s antecedent part, and a strategy for pruning redundant rules from the rule set. We study the performance of our proposed approach for twelve commonly used data sets and compare it with the original AntMiner algorithm, decision tree builder C4.5, Ripper, logistic regression technique, and a SVM. Experimental results show that the accuracy rate obtained by AntMiner-C is better than that of the compared algorithms. However, the average number of rules and average terms per rule are higher.     

无支持度约束的靶向式关联规则挖掘

为解决不考虑支持度时关联规则挖掘中数据项集组合爆炸引起的信息湮灭问题,给出全属性项目集、完全关联规则、关联规则的关键前提等概念。证明以关键前提的超集作为前提的关联规则也一定是完全关联规则,即向上闭合特性。根据该原理设计一个能够消除大量冗余关联规则的靶向式关联规则挖掘算法。通过挖掘实例验证了该算法的可行性和有效性。     

Design and simulation of a parallel inference machine architecture for rule based systems

This paper presents an architecture of the inference machine for a rule based expert system. The paper, structured around the concept of “inference flow graphs”, is aimed at incorporating parallelism in antecedent matching to find out the firable rules as well as firing more than one rule simultaneously, whenever required. Through this architecture, the number of comparisons required during the antecedent matching phase, is significantly reduced. The flow of inferencing can also proceed in a pipelined manner resulting in faster inferences.