一种基于模糊规则的Multi-agent自动谈判策略

针对当前电子商务中基于Agent的谈判系统的谈判策略的静态性问题,提出基于模糊规则的动态谈判策略。Agent在谈判中能根据对方心理因素和动态确定自己的谈判策略后,产生新一轮的提议,帮助用户做出最优的交易决策,且自动地选择合适的策略。分析表明,采用基于模糊规则的策略比采用固定的策略的谈判结果更让用户感到满意。     

Computational Intelligence for Evolving Trading Rules

This paper describes an adaptive computational intelligence system for learning trading rules. The trading rules are represented using a fuzzy logic rule base, and using an artificial evolutionary process the system learns to form rules that can perform well in dynamic market conditions. A comprehensive analysis of the results of applying the system for portfolio construction using portfolio evaluation tools widely accepted by both the financial industry and academia is provided.      

受限模糊规则进化的入侵检测

基于生物进化机理,提出了模糊规则进化的推理方法,并对初始群体产生加以控制,实现受限的模糊规则进化的入侵检测“LEFRIDS”。该方法可以有效控制规则数,加速检测过程,并且由于变异会产生新的规则,使检测规则不断“新陈代谢”,楗高了对未知攻击的检测能力。     

Methodology for Evolving Fuzzy Kalman Filter Identification

International Journal of Control, Automation and Systems - A methodology based on the smart combination of evolving fuzzy clustering and OKID (Observer/ Kalman Filter Identification) algorithm, is...     

An Evolving Fuzzy Predictor for Industrial Applications

A reliable and online predictor is very useful to a wide array of industries to forecast the behavior of time-varying dynamic systems. In this paper, an evolving fuzzy system (EFS) is developed for system state forecasting. An evolving clustering algorithm is proposed for cluster generation. Clusters are established and modified based on constraint criteria of mapping consistence and compatible measurement. A novel recursive Levenberg–Marquardt (R-LM) method is proposed for online training of nonlinear EFS parameters. The viability of the developed EFS predictor is evaluated based on both simulation from benchmark data and real-time tests corresponding to machinery condition monitoring and material property testing. Test results show that the developed EFS predictor is an effective and accurate forecasting tool. It can capture the system's dynamic behavior quickly and track the system's characteristics accurately. The proposed clustering algorithm is an effective structure identification method. The recursive training technique is computationally efficient, and can effectively improve reasoning convergence.      

可进化的入侵检测系统的模糊分类器研究

由于计算机网络中的正常行为和异常行为难以很好界定,所以许多入侵检测系统经常产生误报警。使用模糊逻辑推理方法,入侵检测系统的误报率则会明显降低,可以在入侵检测系统中,使用一套模糊规则和作用在该集合上的模糊推理算法,来判断是否发生了入侵事件。这种方法面临的主要问题是要有一个针对入侵检测的好的模糊算法。该文提出了一种使用遗传算法产生模糊分类器,以检测误用和入侵事件。主要思想是生成两个进化规则子集合,一个用于描述正常行为,一个用于描述异常行为。其中,正常行为规则进化信息来自正常使用时的操作行为,异常行为规则进化信息来自计算机网络受到入侵时的操作行为。     

模糊逻辑控制器的演化硬件实现

讨论在演化硬件（EHW）平台上实现模糊逻辑控制器的演化,模糊逻辑控制器是由一些if-the。规则的集合和具有模糊逻辑的输入输出语言术语特点的一些成员属性函数所构成,这里的演化硬件平台是指可编程模拟选择器阵列,通过遗传算法（GA）演化出的电路具有很强的自适应、自修复能力。     

模糊关联规则及挖掘算法

本文提出了模糊关联规则的概念,研究了模糊关联规则的性质和挖掘算法,同时还提出了一种新的规则有越性的度量函数。     

基于模糊聚类的模糊关联规则挖掘

通过模糊聚类，从已知数据中得到数据点对数据类的隶属度，并以此进行模糊关联规则的挖掘，从而使得模糊关联规则的发现不依赖于人类专家预先给出的隶属度函数；并且实验表明。聚类并没有带来显著的顿外计算时间，对于大型数据库，文章提出的方法是有效的。     

一种自适应的模糊关联规则挖掘算法

关联规则是数据挖掘的重要研究内容之一。传统的关联规则挖掘算法仅适于处理二元属性与分类属性。为更好地处理数量属性,提出了一种自适应的基于模糊概念的量化关联规则挖掘算法。该算法克服了传统的离散分区法的不足,改进了已有模糊关联规则支持度的计算方法。引入了一种基于聚类的隶属函数自动生成方法,使得模糊关联规则的发现不依赖于人类专家给出的隶属函数,使得关联规则的表示自然、简明,有利于专家理解。实验表明该算法是有效的。     

一种自适应的模糊关联规则挖掘算法

关联规则是数据挖掘的重要研究内容之一.传统的关联规则挖掘算法仅适于处理二元属性与分类属性.为更好地处理数量属性,提出了一种自适应的基于模糊概念的量化关联规则挖掘算法.该算法克服了传统的离散分区法的不足,改进了已有模糊关联规则支持度的计算方法.引入了一种基于聚类的隶属函数自动生成方法,使得模糊关联规则的发现不依赖于人类专家给出的隶属函数,使得关联规则的表示自然、简明,有利于专家理解.实验表明该算法是有效的.     

Learning Fuzzy Rules from Data

We present an algorithm, FUZZEX, for learning fuzzy rules from a corpus of data mapping input antecedents to output consequents. The input and output spaces are first divided into a grid of cells and primitive if % then rules formulated from each occupied input cell in the role of an antecedent The distribution of output cells into which data in the input cell maps, plays the role of the consequent interpreted as a fuzzy set. Those input cells associated with sufficiently similar fuzzy output sets are then combined to form a composite rule. A concise set of rules in Disjunctive Normal Form (DNF) is formed by combining adjacent input cells belonging to the same rule, thereby simplifying the logical expression of the antecedents. Optionally, more succinctness of expression may be obtained by recruiting into a rule, adjacent input cells with (little or) no data, but which happen to simplify rule expression. Preliminary testing on testbed datasets is presented. FUZZEX can be applied effectively to problems of large dimensionality.     

模糊关联规则的加权挖掘算法

从实际应用角度，在对模糊关联规则挖掘算法进行改进的基础上，提出了模糊关联规则的加权挖掘算法（FARMA），以及简化的关联规则的加权挖掘算法（SFARMA），通过实验验证了算法的可行性，并对算法的性能进行了讨论。     

运用模糊集挖掘数量属性数据的关联规则

绝大多数关联规则的挖掘方法基于布尔属性数据,但在现实应用中会经常需要对数量属性的数据进行关联挖掘。该文就提出一种算法,在经典Apriori后选集算法的基础上引入了模糊逻辑集合的概念,将数据集中的数量属性按照模糊集合定义进行划分从而将原始事务数据转化成基于模糊集的数据,然后再运用Apriori算法发现潜在的关联规则。     

模糊关联规则的加权挖掘算法

从实际应用角度,在对模糊关联规则挖掘算法进行改进的基础上,提出了模糊关联规则的加权挖掘算法(FARMA),以及简化的关联规则的加权挖掘算法(SFARMA),通过实验验证了算法的可行性,并对算法的性能进行了讨论.     

A new systematic design for Habitually Linear Evolving TS Fuzzy Model

In this paper, a systematic design of habitually evolving Takagi-Sugeno (TS) fuzzy systems, suggested for online prediction of processes with uncertainty, is introduced. A Habitually Linear Evolving Fuzzy System (HLEFS) starts off with an adaptive linear model and evolves into a TS fuzzy model whenever the linear model is unable to mitigate the output error. The number of rules in the HLEFS is controlled by an adaptive threshold on the error. The structure of the HLEFS tends to return to the adaptive linear model as soon as possible, and that is why we have dubbed the proposed model ‘Habitually’ Linear. Three theorems are stated and proved in a sequence in support of the HLEFS ability to keep the output error in a relatively small bound. It is shown that the adaptive linear model may not be good enough when the process changes abruptly and nonlinearly—what we call a Transient Significant Disturbance. In this case, it is proved that evolving into a TS fuzzy system with the proposed algorithm can mitigate the error.The performance of HLEFS in forecasting of daily electrical power consumption is studied and compared with that of four famous existing evolving fuzzy systems. Obtained results demonstrate the applicability and effectiveness of the proposed method in keeping the prediction error low with less number of fuzzy rules.     

基于模糊关联规则挖掘的模糊入侵检测

论文把模糊关联规则挖掘算法引入到网络的入侵检测,利用该算法从网络数据集中提取出具有较高可信性和完备性的模糊规则,并利用这些规则设计和实现用于入侵检测的模糊分类器。同时,针对模糊关联规则挖掘算法,利用K-means聚类算法建立属性的模糊集和模糊隶属函数,并提出了一种双置信度算法以增加模糊规则的有效性和完备性。最后,给出了详实的实验过程和结果,以此来验证提出的模糊入侵检测方法的有效性。     

Modeling Agent Negotiation Via Fuzzy Constraints in E-Business

In e-business, disputes between two or more parties arise for various reasons and involve different issues. Thus, resolution of these disputes frequently relies on some form of negotiation. This article presents a general problem-solving framework for modeling multi-issue multilateral agent negotiation using fuzzy constraints in e-business. Fuzzy constraints are thus used not only to define each agent's demands involving human concepts, but also to represent the relationships among agents. A concession strategy, based on fuzzy constraint-based problem-solving, is proposed to relax demands, and a trade-off strategy is presented to evaluate existing alternatives. This approach provides a systematic method for reaching an agreement that benefits all agents with a high satisfaction degree of constraints. Meanwhile, by applying the method, agents can move toward an agreement more quickly, because their search focuses only on the feasible solution space. An example application to negotiate an insurance policy among agents is provided to demonstrate the usefulness and effectiveness of the proposed framework.     

对演变数据进行关联规则挖掘的新方法

针对已有经常性周期关联规则在演变数据和周期长度方面的局限性,文章提出一种新的方法,从而可以对演变数据进行经常性长周期关联规则的挖掘,这种方法针对演变数据的动态环境,通过对数据块的动态聚类得到周期分段,然后在每一分段内利用低支持度关联规则挖掘算法来发现周期较长的关联规则,整个算法可以在GEMM算法的基础上进行动态模式保持。