Applications of fuzzy sets to rule-based expert system development

In this paper, problems of implementing rule-based expert systems using fuzzy sets are considered. A fuzzy logic software development shell is used that allows inclusion of both crisp and fuzzy rules in decision making and process control problems. Results are given that compare this type of expert system to a human expert in some specific applications. Advantages and disadvantages of such systems are discussed.     

Expert system development methodology and the transition from prototyping to operations: Fiesta, a case study

A major barrier in taking expert systems from prototype to operational status involves instilling end user confidence in the operational system. End users want assurances that their systems have been thoroughly tested, meet all their specifications and requirements, and are built based on designs which are reliable and maintainable. For most software systems, the waterfall life cycle model can provide those assurances. However, this model is inappropriate for expert system development, where an iterative refinement approach is commonly employed. This paper will look at different life cycle models and explore the advantages and disadvantages of each when applied to expert system development. The Fault Isolation Expert System for TDRSS Applications (FIESTA) is presented as a case study example of development of an expert system. FIESTA is planned for use in the Network Control Center (NCC) at Goddard Space Flight Center in Greenbelt, Maryland. The end user confidence necessary for operational use of this system is accentuated by the fact that it will handle real-time data in a secure environment, allowing little tolerance for errors. The paper discusses how FIESTA is dealing with transition problems as it moves from an off-line standalone prototype to an on-line real-time system.     

Automating reuse of software for expert system analysis of remotesensing data

Systems involving remote sensing analysis for airborne and satellite data in combination with geographic information systems are large and complex. The Canada Centre for Remote Sensing (CCRS) has created an expert system shell and several expert systems in order to provide image analysis programs with the necessary knowledge to solve difficult image processing problems, such as updating a forest inventory geographic information system. An interactive task interface (ILTI) provides an expert system with a Prolog module designed to answer queries from the image analysis program by retrieving knowledge from an image analysis knowledge base, the analyst advisor. Image analysis experts currently create ILTI's. They have found this to be a time-consuming task. An incremental/adaptive planner has been developed that will create a plan that emulates the ILTI's behavior by analyzing image processing session dialogues between a human expert and an image analysis program for several cases of forest updates. The planner relies on a knowledge base in order to generalize and modify plans acquired from session dialogues. The planner speeds and simplifies the creation of new expert systems     

A framework for fuzzy expert system creation--application to cardiovascular diseases.

A methodology for the automated development of fuzzy expert systems is presented. The idea is to start with a crisp model described by crisp rules and then transform them into a set of fuzzy rules, thus creating a fuzzy model. The adjustment of the model's parameters is performed via a stochastic global optimization procedure. The proposed methodology is tested by applying it to problems related to cardiovascular diseases, such as automated arrhythmic beat classification and automated ischemic beat classification, which, besides being well-known benchmarks, are of particular interest due to their obvious medical diagnostic importance. For both problems, the initial set of rules was determined by expert cardiologists, and the MIT-BIH arrhythmia database and the European ST-T database are used for optimizing the fuzzy model's parameters and evaluating the fuzzy expert system. In both cases, the results indicate an escalation of the performance from the simple initial crisp model to the more sophisticated fuzzy models, proving the scientific added value of the proposed framework. Also, the ability to interpret the decisions of the created fuzzy expert systems is a major advantage compared to "black box" approaches, such as neural networks and other techniques.     

Validation and verification of expert systems

Validation and verification (V&V) are procedures used to evaluate system structure or behavior with respect to a set of requirements. Although expert systems are often developed as a series of prototypes without requirements, it is not possible to perform V&V on any system for which requirements have not been prepared. In addition, there are special problems associated with the evaluation of expert systems that do not arise in the evaluation of conventional systems, such as verification of the completeness and accuracy of the knowledge base. The criticality of most National Aeronautics and Space Administration (NASA) missions makes it important to be able to certify the performance of the expert systems used to support these missions. This paper presents recommendations for the most appropriate methods for integrating V&V into the Expert System Development Methodology (ESDM) and suggestions for the most suitable approaches for each stage of ESDM development.     

A Framework for Fuzzy Expert System Creation—Application to Cardiovascular Diseases

A methodology for the automated development of fuzzy expert systems is presented. The idea is to start with a crisp model described by crisp rules and then transform them into a set of fuzzy rules, thus creating a fuzzy model. The adjustment of the model's parameters is performed via a stochastic global optimization procedure. The proposed methodology is tested by applying it to problems related to cardiovascular diseases, such as automated arrhythmic beat classification and automated ischemic beat classification, which, besides being well-known benchmarks, are of particular interest due to their obvious medical diagnostic importance. For both problems, the initial set of rules was determined by expert cardiologists, and the MIT-BIH arrhythmia database and the European ST-T database are used for optimizing the fuzzy model's parameters and evaluating the fuzzy expert system. In both cases, the results indicate an escalation of the performance from the simple initial crisp model to the more sophisticated fuzzy models, proving the scientific added value of the proposed framework. Also, the ability to interpret the decisions of the created fuzzy expert systems is a major advantage compared to "black box" approaches, such as neural networks and other techniques.     

Expert systems: Artificial intelligence applied

This article provides an overview of expert systems, the topic of greatest current interest in Artificial Intelligence (AI). The article describes what an expert system is and sketches the mechanisms by which several of the better known systems operate. The prerequisites for constructing an expert system are given and the state of the art of current expert systems is indicated, with an extensive listing of these systems being provided. The article concludes with a forecast of the nature and applications of future expert systems.     

Artificial intelligence in power system operations

Power system operators often reach a cognitive barrier when information arrives too fast during a power system emergency. At such times it becomes difficult to reach a correct diagnosis of the problem or to formulate the correct decision when actions must be taken. Artificial Intelligence gives designers of Energy Management Systems a way to solve many of the diagnosis and decision problems so as to make the EMS more useful. This paper explores reasons why AI techniques, such as knowledge-based expert systems, are being used in EMS designs and the differences between knowledge-based expert systems and traditional numeric algorithm development. The differences between expert systems and the numeric approach extend to the basic conception and design of the applications. This is illustrated using a relay fault diagnosis system, showing both the traditional and rapid prototyping approaches to its development. Finally, issues concerned with the implementation of AI in EMS computers are explored along with the authors' predictions of possible AI applications to power system operations.     

Historical review of OCR research and development

Research and development of OCR systems are considered from a historical point of view. The historical development of commercial systems is included. Both template matching and structure analysis approaches to R&D are considered. It is noted that the two approaches are coming closer and tending to merge. Commercial products are divided into three generations, for each of which some representative OCR systems are chosen and described in some detail. Some comments are made on recent techniques applied to OCR, such as expert systems and neural networks, and some open problems are indicated. The authors' views and hopes regarding future trends are presented     

基于多系统数据接口的整合技术研究与实现

针对"一站妥"工作中存在工作周期长、无法提前进行故障定位和分析等问题,东莞供电局设计了一种工作效率高、快速定位故障的城市用户故障排查专家系统。文章提出了一种基于SOA的Web Service技术,该技术实现了多系统数据接口整合,让城市用户故障排查专家系统可以访问外部系统。专家系统的实现,解决了"一站妥"工作中存在的问题,提高工作效率和客户满意度,更节省更环保。     

Expert system, fuzzy logic, and neural network applications inpower electronics and motion control

Artificial intelligence (AI) tools, such as expert systems, fuzzy logic, and neural networks are expected to usher a new era in power electronics and motion control in the coming decades. Although these technologies have advanced significantly and have found wide applications, they have hardly touched the power electronics and machine drives area. The paper describes these AI tools and their application in the area of power electronics and motion control. The body of the paper is subdivided into three sections which describe, respectively, the principles and applications of expert systems, fuzzy logic, and neural networks. The theoretical portion of each topic is of direct relevance to the application of power electronics. The example applications in the paper are taken from the published literature     

Expert systems in network management-the second revolution

Expert systems are discussed as the next major evolutionary step in network management. Specific examples are presented that demonstrate the benefits of increased programmer productivity, automating network operations, and identifying and diagnosing network problems. Internal structures are discussed. An expert system, functioning in a live environment, that can learn from experience and modify its behavior is presented. Current technological problems in implementation are reviewed and proposed solutions are given     

机械产品智能概念设计专家系统的研究与开发

通过总结专家进行概念设计的设计方式，分析了人工智能解决问题的方法，提出了智能概念设计专家系统的一种模式。应用这一模式，构造了简单机械产品智能概念设计专家系统并得以实现。     

Analog integrated filter compilation

A review of the progress in automated design of analog integrated filters is presented. Such tools are ahead of other analog circuit automation in terms of the acceptance by designers and practical applicability. A survey of the present-day commercial and academic systems is made and the range of facilities available is compared. The problems faced in the design of this type of software are typical of the problems of analog design systems in general; lack of openness for introduction of new design knowledge, difficulties of dealing simultaneously with expert and novice users, poor integration in design environments, and user-interface problems. The structure of a typical system is studied and the computer methods used within are discussed with regard to such issues as speed, flexibility, and ease-of-use. Some future directions for analog filter compilers are proposed.     

Expert systems and telecommunications

First there were databases and database management systems. Then information bases developed, whereby management information systems and decision support systems sprouted. Now, there are knowledge bases on which expert systems are being built. The age of “knowledge” is upon us, and computer programs are being developed to perserve this knowledge. These computer programs are “expert systems” which mimic the behavior of human experts in some specified domain of knowledge. This article addresses these expert systems and gives some applications in the telecommunications area.     

On the uses of expert judgment on complex technical problems

The authors place in perspective the role and uses of expert judgment in examining complex technical and engineering problems. Specifically, they indicate how expert judgements are usually used in analyzing technical problems, how to improve the use of expert judgments, and how to interpret judgments in analysis. The value of quantifying expert judgments to complement the expert's qualitative thinking and reasoning is stressed. The relationships between procedures to quantify judgments and the general principles of engineering are discussed     

New trends in intelligent system design for embedded andmeasurement applications

Intelligent systems adopt soft-computing techniques (encompassing neural networks, fuzzy logic, genetic algorithms, and expert systems) to solve complex problems by mimicking human reasoning. On the other hand, conventional algorithmic approaches are extremely powerful and efficient in tackling applications for which a procedural solution can be easily defined. By themselves, each of these techniques may be the optimal solution for a subproblem, but not efficient enough to solve the problem as a whole. Composite systems, consisting of conventional and soft-computing components in cooperation, are now more than a promise to face complex application needs. In this article we present recent advances in the design of composite systems, with specific reference to embedded and measurement applications     

Survey of knowledge-based systems in power systems: Europe

A survey of the development and experience with knowledge-based systems applied to power systems in Europe, including expert systems used in operation, is presented. The technical aspects of power system operation are briefly reviewed, the role of computers in power systems is discussed, and the motivation for investigating knowledge-based applications in this context is identified. The structure of the research and industrial organizations involved in the development and implementation is described, and the characteristics of main application areas are discussed. Future expert system development and implementation are discussed