A framework for rule-base evidential reasoning in the interval setting applied to diagnosing type 2 diabetes

A new framework for rule-base evidential reasoning in the interval setting is presented. While developing this framework, two collateral problems such as combining and normalizing interval-valued belief structures from different sources and comparing resulting belief intervals, the bounds of which are intervals, arise. The first problem is solved with the use of the so-called “interval extended zero” method. It is shown that interval valued results of the proposed approach to combining and normalizing interval-valued belief structures are enclosed in those obtained by known methods and possess three desirable intuitively obvious properties of normalization procedure defined in the paper. The second problem is solved using the method for interval comparison based on the Demposter-Shafer theory providing the interval valued results of comparison. The advantages of the proposed framework for rule-base evidential reasoning in the interval setting are demonstrated using the developed expert system for diagnosing type 2 diabetes.     

A Forex trading expert system based on a new approach to the rule-base evidential reasoning

Currently FOREX (foreign exchange market) is the largest financial market over the world. Usually the Forex market analysis is based on the Forex time series prediction. Nevertheless, trading expert systems based on such predictions do not usually provide satisfactory results. On the other hand, stock trading expert systems called also “mechanical trading systems”, which are based on the technical analysis, are very popular and may provide good profits. Therefore, in this paper we propose a Forex trading expert system based on some new technical analysis indicators and a new approach to the rule-base evidential reasoning (RBER) (the synthesis of fuzzy logic and the Dempster–Shafer theory of evidence). We have found that the traditional fuzzy logic rules lose an important information, when dealing with the intersecting fuzzy classes, e.g., such as Low and Medium and we have shown that this property may lead to the controversial results in practice. In the framework of the proposed in the current paper new approach, an information of the values of all membership functions representing the intersecting (competing) fuzzy classes is preserved and used in the fuzzy logic rules. The advantages of the proposed approach are demonstrated using the developed expert system optimized and tested on the real data from the Forex market for the four currency pairs and the time frames 15 m, 30 m, 1 h and 4 h.     

A hybrid stock trading system for intelligent technical analysis-based equivolume charting

This paper presents the use of an intelligent hybrid stock trading system that integrates neural networks, fuzzy logic, and genetic algorithms techniques to increase the efficiency of stock trading when using a volume adjusted moving average (VAMA), a technical indicator developed from equivolume charting. For this research, a neuro–fuzzy-based genetic algorithm (NF-GA) system utilizing a VAMA membership function is introduced. The results show that the intelligent hybrid system takes advantage of the synergy among these different techniques to intelligently generate more optimal trading decisions for the VAMA, allowing investors to make better stock trading decisions.     

A fuzzy expert system shell using both exact and inexact reasoning

This paper presents a comprehensive expert system shell which can deal with both exact and inexact reasoning. A prototype of this proposed shell, code named as SYSTEM Z-IIe, has been implemented successfully. It is a rule-based system which employs fuzzy logic and numbers for its reasoning. Two basic inexact concepts, fuzziness and uncertainty, are both used and distinct from each other clearly in the system. Moreover, these two concepts have been built into two levels for inexact reasoning, i.e. the level of the rules and facts, and the level of the values of the objects of these rules and facts. Other features of Z-IIe include multiple fuzzy propositions in rules and dual fact input mechanisms. It also allows any combinations of fuzzy and normal terms and uncertainties. Fuzzy numeric comparison logic control is also available for the rules and facts. Its natural language interface which uses English with restricted syntax improves the efficiency of knowledge engineering. Z-IIe is also coupled to a Database Management System for supplying facts from existing databases if appropriate. All these features can be combined to build very powerful expert systems and are illustrated by an example.     

一种基于自适应证据推理规则的集成学习方法

当前集成学习中的结合策略难以兼顾各个基学习器之间的信息和模型的可解释性。使用证据推理（evidential reasoning,ER）规则作为结合策略,将各个基学习器结果作为证据参与融合,可以较好地解决以上问题。但传统ER规则的证据参数是单一的,对不同的基学习器模型使用相同的证据参数显然是不合理的。为此,提出一种基于自适应证据推理（adaptive-evidential reasoning,A-ER）规则的集成学习方法,该方法在每次证据融合前对证据的类别进行判断,针对不同的证据类别自适应分配不同的证据参数。通过不同的分类案例表明,该方法与案例中其他方法相比具有更高的分类精度,证明了该方法使证据参数设置更加合理且具有更好的可解释性和泛化能力。     

可拓学理论在信息融合中的应用研究

基本概率分配函数的选取是实际应用D-S证据推理的信息融合算法的难题之一。探讨了可拓集理论和关联函数的本质,提出对关联函数进行归一化处理,并以归一化后的数据作为证据体对分类命题的可信度分配,利用Dempster组合规则生成总体可信度分配,实现多源信息的融合。仿真计算结果表明,该算法是可靠的和有效的。     

Fault diagnosis and cause analysis using fuzzy evidential reasoning approach and dynamic adaptive fuzzy Petri nets

Fault diagnosis is of great importance to all kinds of industries in the competitive global market today. However, as a promising fault diagnosis tool, fuzzy Petri nets (FPNs) still suffer a couple of deficiencies. First, traditional FPN-based fault diagnosis methods are insufficient to take into account incomplete and unknown information in diagnosis process. Second, most of the fault diagnosis methods using FPNs are only concerned with forward fault diagnosis, and no or less consider backward cause analysis. In this paper, we present a novel fault diagnosis and cause analysis (FDCA) model using fuzzy evidential reasoning (FER) approach and dynamic adaptive fuzzy Petri nets (DAFPNs) to address the problems mentioned above. The FER is employed to capture all types of abnormal event information which can be provided by experts, and processed by DAFPNs to identify the root causes and determine the consequences of the identified abnormal events. Finally, a practical fault diagnosis example is provided to demonstrate the feasibility and efficacy of the proposed model.     

基于证据推理和置信规则库的装备寿命评估

针对航天产品试验样本少,寿命评估难的特点,结合产品在研制阶段多种工作环境的失效数据,提出了一种基于证据推理(evidential reasoning,ER)和置信规则库(belief-rule-base,BRB)进行装备寿命评估的新方法.首先,分析了模型的合理性并使用多维BRB模型将多种环境下的寿命数据折合为标准工作环境下的寿命数据,然后通过ER算法将折合后数据和实际工作环境数据进行融合.其次,详细说明了BRB--ER模型的推理过程和寿命评估的步骤.最后,采用某航天产品的失效数据对该方法进行了验证,并用已有的产品寿命的固定值进行BRB的参数更新.研究结果表明,在专家知识准确合理时,该模型能够准确地评估产品寿命,并可根据已有的产品的固定寿命进行训练,建立更加准确的寿命预测模型.     

Linguistic hesitant fuzzy multi-criteria decision-making method based on evidential reasoning

Linguistic hesitant fuzzy sets (LHFSs), which can be used to represent decision-makers’ qualitative preferences as well as reflect their hesitancy and inconsistency, have attracted a great deal of attention due to their flexibility and efficiency. This paper focuses on a multi-criteria decision-making approach that combines LHFSs with the evidential reasoning (ER) method. After reviewing existing studies of LHFSs, a new order relationship and Hamming distance between LHFSs are introduced and some linguistic scale functions are applied. Then, the ER algorithm is used to aggregate the distributed assessment of each alternative. Subsequently, the set of aggregated alternatives on criteria are further aggregated to get the overall value of each alternative. Furthermore, a nonlinear programming model is developed and genetic algorithms are used to obtain the optimal weights of the criteria. Finally, two illustrative examples are provided to show the feasibility and usability of the method, and comparison analysis with the existing method is made.     

Network forensics based on fuzzy logic and expert system

Network forensics is a research area that finds the malicious users by collecting and analyzing the intrusion or infringement evidence of computer crimes such as hacking. In the past, network forensics was only used by means of investigation. However, nowadays, due to the sharp increase of network traffic, not all the information captured or recorded will be useful for analysis or evidence. The existing methods and tools for network forensics show only simple results. The administrators have difficulty in analyzing the state of the damaged system without expert knowledge. Therefore, we need an effective and automated analyzing system for network forensics. In this paper, we firstly guarantee the evidence reliability as far as possible by collecting different forensic information of detection sensors. Secondly, we propose an approach based on fuzzy logic and expert system for network forensics that can analyze computer crimes in network environment and make digital evidences automatically. At the end of the paper, the experimental comparison results between our proposed method and other popular methods are presented. Experimental results show that the system can classify most kinds of attack types (91.5% correct classification rate on average) and provide analyzable and comprehensible information for forensic experts.     

A fuzzy expert system for noise-induced sleep disturbance

In this paper, an attempt has been made to develop a fuzzy expert system for predicting the effects of sleep disturbance by noise on humans as a function of noise level, age, and duration of its occurrence. The modelling technique is based on the concept of fuzzy logic, which offers a convenient way of representing the relationships between the inputs and outputs of a system in the form of IF-THEN rules. It has been established on the basis of findings of various researchers that the effect of noise on sleep disturbance depends to a large extent on age. The middle-aged people have more probability of sleep disruption than the young people at the same noise levels. However, very little difference is found in sleep disturbance due to noise between young and old people. In addition, the duration of occurrence of noise is an important factor in determining the sleep disturbance over the limited range from few seconds to few minutes. Finally, we have compared our model results with some of the findings of researchers reported in International Journals.     

基于证据推理的联合故障检测方法

针对卡方故障检测方法对软故障的检测性能较差, 甚至会导致滤波器发散的问题, 提出一种基于证据推理的联合故障检测方法. 将组合导航中的各子滤波器作为证据, 利用每个子滤波器的状态及协方差构造联合故障检测函数, 并利用联合故障检测函数的概率分布计算基本置信指派, 再将多个证据按D-S 规则进行融合, 根据融合结果进行故障检测. 仿真结果表明, 所提出的方法对硬故障的检测性能与卡方故障检测性能相当, 但对软故障的检测性能要优于卡方故障检测, 可提高组合导航系统的可靠性和精度.

     

基于Agent的专家系统推理模型

针对传统专家系统推理模型结构在知识获取方面适应性差的现状,从系统科学的视角,运用复杂适应系统理论,对传统专家系统的结构及运行机制进行了改进.引入Agent来模拟人脑中的神经元,用来承载专家系统中相互作用的知识,然后,基于Multi-Agent之间的相互作用来构建复杂适应的专家系统推理模型.从而,将专家系统中的知识获取机制、知识库、推理机三者统一于由Multi-Agent进行相互作用的复杂适应系统之中.通过设计体育赛事申办决策专家系统的原型,进行了专家系统推理模型的验证.原型运行结果表明:基于Multi-Agent的专家系统推理模型结构能够有效地提高专家系统知识获取的适应性.这为研究更加接近人脑智能的专家系统提供了崭新的研究思路.     

A fuzzy expert system for optimizing parameters and predicting performance measures in hard-milling process

The main focus of research in hard-milling domain has been the enhancement of tool life and the improvement in workpiece surface quality. This paper deals with the application of expert system technology in order to use the experimental data for optimization of milling parameters so as to achieve targets of enhancing tool life and improving workpiece surface finish. Hard-milling experiments were conducted to study the effects of workpiece material hardness, cutter’s helix angle, milling orientation and coolant upon tool life, workpiece surface roughness, and cutting forces. The experimental data were converted to useful information using ANOVA and numeric optimization, and this information was used to develop the knowledge-base in form of IF–THEN rules. Expert system utilized fuzzy logic for its reasoning mechanism, while, fuzzy data sets and crisp sets were freely mixed in antecedents and consequents of the rules. Effectiveness of the expert system was based upon two modules, namely optimization module and prediction module, with each of them operating upon different set of rules. Optimization module provides the optimal selection and combination of aforementioned milling parameters according to the desired objective, while the prediction module provides the prediction of performance measures for the combination of parameters finalized by the optimization module.     

Web shopping expert using new interval type-2 fuzzy reasoning

Finding a product with high quality and reasonable price online is a difficult task due to uncertainty of Web data and queries. In order to handle the uncertainty problem, the Web Shopping Expert, a new type-2 fuzzy online decision support system, is proposed. In the Web Shopping Expert, a fast interval type-2 fuzzy method is used to directly use all rules with type-1 fuzzy sets to perform type-2 fuzzy reasoning efficiently. The parameters of type-2 fuzzy sets are optimized by a least square method. The Web Shopping Expert based on the interval type-2 fuzzy inference system provides reasonable decisions for online users.     

基于模糊AHP和证据理论的混合决策模型

针对模糊层次分析法（Fuzzy AHP,FAHP）用于产品方案评价时存在的逆向排序问题和不确定信息处理问题,分析其原因后将不确定性推理的证据推理（Evidential Reasoning,ER）理论引入FAHP的层次结构进行底层方案评价值的计算,在此基础上提出了FAHP-ER混合决策模型,该模型由于ER的引入而大大提高了它在不确定信息处理方面的能力,从而克服了FAHP方法对不确定信息处理不足的问题。用一个轴承转子系统设计方案的评价实例对混合决策模型进行了验证,很好地处理了方案评价过程中决策者的各种主观不确定信息,在此基础上获得了最佳的转子设计方案。     

A novel fuzzy evidential reasoning paradigm for data fusion with applications in image processing

This paper presents a novel data fusion paradigm based on fuzzy evidential reasoning. A new fuzzy evidence structure model is first introduced to formulate probabilistic evidence and fuzzy evidence in a unified framework. A generalized Dempster’s rule is then utilized to combine fuzzy evidence structures associated with multiple information sources. Finally, an effective decision rule is developed to take into account uncertainty, quantified by Shannon entropy and fuzzy entropy, of probabilistic evidence and fuzzy evidence, to deal with conflict and to achieve robust decisions. To demonstrate the effectiveness of the proposed paradigm, we apply it to classifying synthetic images and segmenting multi-modality human brain MR images. It is concluded that the proposed paradigm outperforms both the traditional Dempster–Shafer evidence theory based approach and the fuzzy reasoning based approach     

A fuzzy expert database system

Fuzzy concepts always exist in much of human reasoning as well as decision making. This paper presents a fuzzy expert database system which is an integration of a fuzzy expert system building tool called SYSTEM Z-II and a database management system called Rdb/VMS. This system is able to extract fuzzy data and terms stored in a database and used in the fuzzy reasoning in an expert system. It can also retrieve information by fuzzy database-queries which are generated by the expert system automatically. Many expert systems in different domain areas such as decision making can be constructed. Sample applications are described to demonstrate the flexibility and power of this system. The fuzzy query language defined and used in the system can also be used independently as a fuzzy enquiry tool in database applications.     

Expert system for predicting stock market timing using a candlestick chart

It has been one of the greatest challenges to predict the stock market. Since stock prices vary dramatically, it is important to determine when to buy and sell stocks in order to get high returns from stock investment. In this study, we have developed a candlestick chart analysis expert system, or a chart interpreter, for predicting the best stock market timing. The expert system has patterns and rules which can predict future stock price movements. Defined patterns are classified into five groups with respect to their meanings: falling, rising, neutral, trend-continuation and trend-reversal patterns. The experimental results revealed that the developed knowledge base could provide excellent indicators with an average hit ratio of 72% to help investors get high returns from their stock investment. Through experiments from January 1992 to June 1997, it was proven that the developed knowledge base was time- and field-independent.