智能系统中获取模糊规则的神经网络方法

智能系统中一类重要的定性知识要用模糊集理论中的模糊语言进行描述。本文在研究模糊定性知识形式描述和自组织竞争神经网络特性的基础上，提出了一种从一组具有数值特性的训练样本集中获取隶属函数和模糊规则的神经网络模型和方法。通过对Ｉｒｉｓ数据集的应用实验表明了该方法能对这一类数据进行有效的描述。     

一种模糊知识处理模型

本文介绍一种基于模糊子集理论的知识处理模型ＦＫＰＭ，它适用于专家系统、管理信息系统中模糊信息的处理。本文主要讨论ＦＫＰＭ的模糊知识的表达和模糊推理方法。     

基于定性模糊网络的分层Option 算法

在强化学习的研究中，常用的知识传递方法通过抽取系统最优策略的特征获得知识．由于所获得知识 通常与系统参数有关，因此这些方法难以应用于状态转移概率随系统参数变化的一类任务中．本文提出一种基于定 性模糊网络的分层Option 算法，该算法用定性动作描述系统的次优策略，并用定性模糊网络抽取次优策略的共同特 征获得与参数无关的知识，完成知识传递．倒立摆系统的控制实验结果表明：定性模糊网络能有效地表示各种参数 值不同的倒立摆系统所具有的控制规律，获取与系统参数无关的知识，将常用的知识传递方法从参数无关任务扩展 到参数相关任务中．     

电脑故障分类及其领域知识的表达

在电脑软硬件故障的分类和特点的基础上，抽象出其故障知识的表达模型。采用了基于模糊产生式规则的方法来表达其领域知识，同时阐述了该方法中模糊系数和权系数的确定过程，给出了一个典型的具体实例。证明了该方法的合理性和有效性，实现了一种与电脑故障实际情况相符的较为合理的知识表达方法。     

一种模糊知识库系统及其推理机制研究

介绍了一种具有模糊推理机制的模糊知识系统的基本结构、知识表示和推理机制，阐述了在模糊知识库设计与实现中，模糊推理机构造和工作流程设计的方法。该系统推理机制是基于传统PETE算法的扩展，通过使用相似性方法来处理模糊问题，实现了一种较为理想的不确定性推理；同时系统采用正向和反向推理相结合的双向推理机，使推理具有较高的准确性。最后给出了一个实例验证系统可行性。     

FMS故障诊断的模糊行为Petri网研究

根据ＦＭＳ故障诊断推理中知识的模糊性，提出模糊行为Ｐｅｔｒｉ网（ＦＢＰＮ）的定义，研究用模糊行为Ｐｅｔｒｉ网表示模糊产生式规则的方法，提出一种模糊反向推理机制，给出算法的实现。最后以ＢＦＥＣ—ＦＭＳ的刀库换刀故障为例，证明该方法的可行性和有效性     

一种新的模糊神经元网络学习方法

提出了一种模糊神经元网络的学习算法即利用多 层多层模糊IF/THEN规则表达专家知识的神经网络学习方法，在以此构造的基于多源信息融合的分类系统中，采用了多层模糊IF/THEN规则进行分类。为了处理模糊语言值，提出了一种能够控制模糊输入矢量的神经网络体系结构。该方法能够对非线性实间隔矢量和模糊矢量进行分类，工程实验表明，此学习算法是切实可行的。     

一种基于模糊粗糙集知识获取方法

本文介绍了粗糙集和模糊粗糙集的上下近似。并且利用模糊粗糙上下近似算子,论述了在不完备模糊信息系统中知识获取的一种方法。应用这种方法能够让隐藏在不完备模糊信息系统中的知识,以决策规则的形式表示出来。最后给出了一种实现算法和实例。     

故障仿真预测系统的知识处理

在对某装备故障仿真预测知识分析的基础上，利用模糊数学理论导出了模糊推理运算规则，研究了模糊知识的表示形式和实现方法。此种方法已成功的应用在反后坐装置故障模糊预测系统中。     

模糊知识数据库数据模型及其实现技术

本文提出一种能处理模糊知识的知识数据库数据模型，详细介绍了其模糊知识表示，模糊关系代数及不精确推理的实现策略。     

Mining fuzzy periodic association rules

We develop techniques for discovering patterns with periodicity in this work. Patterns with periodicity are those that occur at regular time intervals, and therefore there are two aspects to the problem: finding the pattern, and determining the periodicity. The difficulty of the task lies in the problem of discovering these regular time intervals, i.e., the periodicity. Periodicities in the database are usually not very precise and have disturbances, and might occur at time intervals in multiple time granularities. To overcome these difficulties and to be able to discover the patterns with fuzzy periodicity, we propose the fuzzy periodic calendar which defines fuzzy periodicities. Furthermore, we develop algorithms for mining fuzzy periodicities and the fuzzy periodic association rules within them. Experimental results have shown that our method is effective in discovering fuzzy periodic association rules.     

Fuzzy rule-based similarity model enables learning from small case bases

The concept of similarity plays a fundamental role in case-based reasoning. However, the meaning of “similarity” can vary in situations and is largely domain dependent. This paper proposes a novel similarity model consisting of linguistic fuzzy rules as the knowledge container. We believe that fuzzy rules representation offers a more flexible means to express the knowledge and criteria for similarity assessment than traditional similarity metrics. The learning of fuzzy similarity rules is performed by exploiting the case base, which is utilized as a valuable resource with hidden knowledge for similarity learning. A sample of similarity is created from a pair of known cases in which the vicinity of case solutions reveals the similarity of case problems. We do pair-wise comparisons of cases in the case base to derive adequate training examples for learning fuzzy similarity rules. The empirical studies have demonstrated that the proposed approach is capable of discovering fuzzy similarity knowledge from a rather low number of cases, giving rise to the competence of CBR systems to work on a small case library.     

关系数据库中模糊规则的快速挖掘算法

关联规则和时序规则是数据挖掘的任务之一.在以往的算法中,规则通常用确定的数值或概念来表示,往往不具有实际意义,而且不容易被用户理解.研究了从大型关系数据库中挖掘模糊关联规则和模糊时序规则的问题.基于模糊集合的理论,提出了两个模糊关联规则的挖掘算法,然后把它们分别扩展为模糊时序规则的挖掘算法.用模糊概念表示的规则更符合人的思维和表达习惯,增强了规则的可理解性.     

Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge

This study proposes a knowledge discovery model that integrates the modification of the fuzzy transaction data-mining algorithm (MFTDA) and the Adaptive-Network-Based Fuzzy Inference Systems (ANFIS) for discovering implicit knowledge in the fuzzy database more efficiently and presenting it more concisely. A prototype was built for testing the feasibility of the model. The testing data are from a company’s human resource management department. The results indicated that the generated rules (knowledge) are useful in supporting the company to predict its employees’ future performance and then assign proper persons for appropriate positions and projects. Furthermore, the convergence of ANFIS in the model was proven to be more efficient than a generic fuzzy artificial neural network.     

Discovery of fuzzy temporal association rules

We propose a data mining system for discovering interesting temporal patterns from large databases. The mined patterns are expressed in fuzzy temporal association rules which satisfy the temporal requirements specified by the user. Temporal requirements specified by human beings tend to be ill-defined or uncertain. To deal with this kind of uncertainty, a fuzzy calendar algebra is developed to allow users to describe desired temporal requirements in fuzzy calendars easily and naturally. Fuzzy operations are provided and users can define complicated fuzzy calendars to discover the knowledge in the time intervals that are of interest to them. A border-based mining algorithm is proposed to find association rules incrementally. By keeping useful information of the database in a border, candidate itemsets can be computed in an efficient way. Updating of the discovered knowledge due to addition and deletion of transactions can also be done efficiently. The kept information can be used to help save the work of counting and unnecessary scans over the updated database can be avoided. Simulation results show the effectiveness of the proposed system. A performance comparison with other systems is also given.     

On extracting recommendation knowledge for personalized web-based learning based on ant colony optimization with segmented-goal and meta-control strategies

Personalized web-based learning has become an important learning form in the 21st century. To recommend appropriate online materials for a certain learner, several characteristics of the learner, such as his/her learning style, learning modality, cognitive style and competency, need to be considered. An earlier research result showed that a fuzzy knowledge extraction model can be established to extract personalized recommendation knowledge by discovering effective learning paths from past learning experiences through an ant colony optimization model. Though that results revealed the theoretical potential of the proposed method in discovering effective learning paths for learners, critical limitations arose when considering its applications in real world situations, such as the requirement of a large amount of learners and a long period of training cycles in order to discover good learning paths for learners. These practical issues motivate this research. In this paper, the aim is to resolve the aforementioned issues by devising more efficient algorithms that basically run on the same ant colony model yet requiring only a reasonable number of learners and training cycles to find satisfactory good results. The key approaches to resolving the practical issues include revising the global update policy, an adaptive search policy and a segmented-goal training strategy. Based on simulation results, it is shown that these new ingredients added to the original knowledge extraction algorithm result in more efficient ones that can be applied in practical situations.     

基于粗集的产品色彩预测系统研究

针对传统人工预测流行色方法效率低、费用高的问题,采用决策表知识表达技术和模糊集合方法构建了流行色知识仓库,结合可辨识矩阵理论和粗集理论提出流行色预测知识挖掘算法,该算法可根据流行色知识库建立条件属性和决策属性依赖关系,从而完成流行色的预测推理。开发了基于粗集理论的智能化流行色预测系统,并以服装产品为例预测流行色测,结果表明该系统可准确预测未来短期内的流行色。     

大规模定制环境下的决策案例库系统

针对大规模定制决策的特点,为实现决策案例的重用,提出了一种面向大规模定制决策问题的案例库系统,设计了基于框架结构的案例知识表示模型,介绍了基于模糊加权的案例相似度计算和匹配算法,该算法解决了大规模定制决策问题的结构化表达、检索匹配和重用问题,开发了案例库的原型系统以支持案例管理、推理和基于案例的规则发现。     

Determining membership functions and minimum fuzzy support in finding fuzzy association rules for classification problems

This paper propose a new method, that employs the genetic algorithm, to find fuzzy association rules for classification problems based on an effective method for discovering the fuzzy association rules, namely the fuzzy grids based rules mining algorithm (FGBRMA). It is considered that some important parameters, including the number and shapes of membership functions in each quantitative attribute and the minimum fuzzy support, are not easily user-specified. Thus, the above-mentioned parameters are automatically determined by a binary string or chromosome is composed of two substrings: one for each quantitative attribute by the coding method proposed by Ishibuchi and Murata, and the other for the minimum fuzzy support. In each generation, the fitness value, which maximizes the classification accuracy rate and minimizes the number of fuzzy rules, of each chromosome can be obtained. When reaching the termination condition, a chromosome with maximum fitness value is then used to test its performance. For classification generalization ability, the simulation results from the iris data and the appendicitis data demonstrate that proposed method performs well in comparison with other classification methods.