A two-stage evolutionary process for designing TSK fuzzy rule-basedsystems

Nowadays, fuzzy rule-based systems are successfully applied to many different real-world problems. Unfortunately, relatively few well-structured methodologies exist for designing and, in many cases, human experts are not able to express the knowledge needed to solve the problem in the form of fuzzy rules. Takagi-Sugeno-Kang (TSK) fuzzy rule-based systems were enunciated in order to solve this design problem because they are usually identified using numerical data. In this paper we present a two-stage evolutionary process for designing TSK fuzzy rule-based systems from examples combining a generation stage based on a (mu, lambda)-evolution strategy, in which the fuzzy rules with different consequents compete among themselves to form part of a preliminary knowledge base, and a refinement stage in which both the antecedent and consequent parts of the fuzzy rules in this previous knowledge base are adapted by a hybrid evolutionary process composed of a genetic algorithm and an evolution strategy to obtain the final Knowledge base whose rules cooperate in the best possible way. Some aspects make this process different from others proposed until now: the design problem is addressed in two different stages, the use of an angular coding of the consequent parameters that allows us to search across the whole space of possible solutions, and the use of the available knowledge about the system under identification to generate the initial populations of the Evolutionary Algorithms that causes the search process to obtain good solutions more quickly. The performance of the method proposed is shown by solving two different problems: the fuzzy modeling of some three-dimensional surfaces and the computing of the maintenance costs of electrical medium line in Spanish towns. Results obtained are compared with other kind of techniques, evolutionary learning processes to design TSK and Mamdani-type fuzzy rule-based systems in the first case, and classical regression and neural modeling in the second.     

基于知识库的决策支持系统关键技术研究

本文将区域循环经济的特点与知识库的原理相结合,研究了决策支持系统中指标的构建和模型选择关键问题,给出了决策模型知识、符号知识、案例知识的表示方法,以及规则和推理机制、知识库的实现原型,为实现指标体系的动态组合,决策方法的智能选择提供了有效的解决方案。     

Concept recognition: An approximate reasoning framework

This article presents a knowledge based methodology for recognizing concept instances in complex data such as natural scenes or speech signals. the architecture of a prototype system performing this task is also described. In order to obtain the capability of handling noisy patterns, the knowledge representation is based on continuous valued logics, and the inference engine is able to do sophisticated reasoning about the knowledge it uses in order to take into account the possible degradation of cues in the pattern. The knowledge base is subdivided into a bulk knowledge and a degradation theory. the bulk knowledge consists of production rules capturing discriminating cues in the signal as they appear in the normal cases. the degradation theory describes how the cues can be degraded owing to insertion and deletion errors. The whole classification process consists of two phases. First of all, the rules of the bulk knowledge are applied to the pattern, trying to obtain a suitable classification, and the evidence assigned to the rules is combined with a technique based on Dempster-Shafer theory. In the second phase, the classification is refined taking into account also the possible degradations in the pattern. the resulting system is then able to deal with different kinds of uncertainty and errors, such as fluctuation in the values measured for the pattern and insertion-deletion errors. the method is illustrated and evaluated on a simple example of spoken word recognition.     

Dependence directed reasoning and learning in systems maintenancesupport

The maintenance of large information systems involves continuous modifications in response to evolving business conditions or changing user requirements. Based on evidence from a case study, it is shown that the system maintenance activity would benefit greatly if the process knowledge reflecting the teleology of a design could be captured and used in order to reason about he consequences of changing conditions or requirements, A formalism called REMAP (representation and maintenance of process knowledge) that accumulates design process knowledge to manage systems evolution is described. To accomplish this, REMAP acquires and maintains dependencies among the design decisions made during a prototyping process, and is able to learn general domain-specific design rules on which such dependencies are based. This knowledge cannot only be applied to prototype refinement and systems maintenance, but can also support the reuse of existing design or software fragments to construct similar ones using analogical reasoning techniques     

FDAS: architecture and implementation

Abstract: FDAS (Fabric Defects Analysis System) is a knowledge-based system (KBS) for diagnosing defects in woven textile structures. The following major issues were considered in the design of FDAS: (1) range of applications; (2) user profiles; (3) response time requirements; (4) modularity and (5) ease of system modification and enhancements. Knowledge about defects is represented in FDAS using a hierarchy of classes, with the slots representing defect attributes, and forward chaining rules. The inferencing process is controlled by slots of another distinct class hierarchy. Inference is made more efficient by hierarchical classification of the defects with pruning. The agenda (i.e. ordered set of hypotheses) is dynamically reset using actions attached to rules. The diagnosis information—information about the causes of the defects and remedial actions to be taken—is kept separate from the rules in the knowledge base. The user interface part of the system is also independent of the knowledge base, which facilitates easier tailoring of the system to meet the needs of different users. The user interaction with FDAS is menu-based and has been designed to minimize cognitive load on the user. FDAS has been extensively evaluated by in-house individuals who are experts in the task of fabric defects analysis. It has also been demonstrated to experts from the industry and is ready for field tests.     

An intelligent system for automatic layout routing in aerospace design

This paper discusses the development of an intelligent routing system for automating design of electrical wiring harnesses and pipes in aircraft. The system employs knowledge based engineering (KBE) methods and technologies for capturing and implementing rules and engineering knowledge relating to the routing process. The system reads a mesh of three dimensional structure and obstacles falling within a given search space and connects source and target terminals satisfying a knowledge base of design rules and best practices. Routed paths are output as computer aided design (CAD) readable geometry, and a finite element (FE) mesh consisting of geometry, routed paths and a knowledge layer providing detail of the rules and knowledge implemented in the process. Use of this intelligent routing system provides structure to the routing design process and has potential to deliver significant savings in time and cost.     

基于改进的BP网络的语法分析系统设计

针对汉语语法分析问题提出了一种基于改进的BP网络的语法分析专家系统的设计方案。其核心是构造存储和管理文法知识的知识库及具有语言专家智能行为和语法分析能力的推理机。本系统中知识库采用产生式规则的知识表达方式，并将知识二元化存储在神经网络中；推理机采用神经网络进行推理。最后给出了系统的运行实例，说明该系统的有效性。     

A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems

The building of intelligent monitoring and diagnostic systems for complex industrial domains tends to be hindered by the knowledge-acquisition bottleneck. Creating good knowledge bases for such tasks is notoriously difficult, especially where human experts are not readily available. High dimensionality of the domain attributes presents a further obstacle for a number of rule-induction algorithms which would, otherwise, have the potential for automating knowledge acquisition. This paper attempts to tackle both problems, by proposing a highly modular framework for data-driven fuzzy ruleset induction incorporating a dimensionality-reduction step based on rough set theory. This removes redundant and information-poor attributes from the data, thereby significantly increasing the speed of the induction algorithm, which is employed to generalise historic data into fuzzy association rules. The aid of dimensionality reduction extends past the training stage of the system into its runtime. By removing information-poor attributes, the implemented system is kept simple by requiring fewer connections to physical instrumentation, while the system’s response times are increased. The paper introduces the techniques jointly forming the proposed framework, and demonstrates the applicability of the approach by building a monitoring system for an urban water treatment plant. The results of this application are presented and discussed, and comparisons to alternative approaches are given.     

A knowledge-based intelligent decision support system for national defense budget planning

In this paper, we present a new framework for knowledge-based intelligent decision support systems for developing a national defense budget planning. The planning procedure for and architecture of the national defense budget in Taiwan are discussed in detail. In particular, the theories and techniques of intelligent decision support are used in the yearly practical budget planning process. Based on data in the financial database and knowledge in the knowledge base, we easily adjust the beforehand budget proposal. Furthermore, a knowledge-based intelligent decision support system has been implemented and it collects a series of rules extracted from national defense experts for successful reasoning. By using forward reasoning and knowledge rules, the system can automatically change and regenerate the national defense budget plan immediately. Finally, the empirical functions of the KIDSS system are also addressed.     

HS编码查询知识库设计与实现

HS(Harmonized System)商品编码体系被进出口监管和统计部门广泛使用.HS编码的智能查询可以为进出口相关企业提供便利,对监管自动化和效率提供有效支持.基于本体的自动分类方法,描述了一种实现HS编码查询的知识库与推理方法,提供了知识库构建、标准和非标准商品名处理、置信度计算等技术细节,给出了原型系统和实验数据.     

基于原型的三维服装款式智能CAD方法

服装业的发展对服装CAD技术的智能化、立体化提出了要求。该文在研究三维服装款式变化规律的基础上,构建三维服装款式数学原型;为了智能控制三维原型的变化,采用三次样条曲线和双三次曲面片构建三维服装原型;分析原型变换控制参数,研究款式变化与原型控制参数之间的关系,总结归纳出款式设计知识,建立相应知识库及推理机制。以服装衣领为例,实现了以款式描述输入驱动的智能衣领设计。     

基于Web的非精确反向推理专家系统研究与实现

介绍了一个基于Web的精确反向推理专家系统原型,通过对其增加非精确推理功能,完成了对此原型的改进,使其能进行非精确分类或诊断。提出在推理过程中对所使用的规则、条件等信息的收集及处理算法,并给出一个推理过程分析实例,论述了对原型改进的基本步骤,同时也提供了一个知识库例子。探讨基于Web的专家系统的应用实践,对于推动专家系统在我国的应用具有现实意义。     

Fuzzy expert system with double knowledge base for ultrasonic classification

This article shows a pattern recognition method for object classification using ultrasonic sensors and a dual knowledge base fuzzy expert system. The developed system uses a pair of ultrasonic sensors for obtaining information about the object shape from the ultrasonic echo signal envelope. In order to reduce the size of the database, a set of parameters is calculated for extracting knowledge about the object. However, the information provided by ultrasonic sensors contains a very high uncertainty level. This uncertainty is caused by several environmental effects, which are very difficult to eliminate in industrial applications. Among these environment factors are the air temperature and humidity, the air movement, etc. They create variations in the proprieties of the medium and disturbances during the acoustic propagation process. The presented system has been specially designed for industrial applications, where it is very difficult to reduce these disturbances and where it is necessary to use intelligent systems with high autonomy. The fuzzy expert system proposed has a dual knowledge base, that is, a statistical knowledge located on the memberships functions, and the standard rule-based knowledge. This expert system deals with the uncertainties in the information, and it is able to generate and modify the knowledge base and the decision rules in an automatic way. Furthermore, it is able to adapt the knowledge base to the slow changes produced by disturbing factors, such as humidity and temperature. On the other hand, because this system maintains a rule-based structure it is very easy to incorporate expert human knowledge.     

Knowledge acquisition and ontology modelling for construction of a control and monitoring expert system

This paper presents the processes of knowledge acquisition and ontology development for structuring the knowledge base of an expert system. Ontological engineering is a process that facilitates construction of the knowledge base of an intelligent system. Ontology is the study of the organization and classification of knowledge. Ontological engineering in artificial intelligence has the practical goal of constructing frameworks for knowledge that allow computational systems to tackle knowledgeintensive problems and it supports knowledge sharing and reuse. To illustrate the process of conceptual modelling using the Inferential Modelling Technique as a basis for ontology construction, the tool and processes are applied to build an expert system in the domain of monitoring of a petroleum-production facility.     

An interactive tool for knowledge base refinement

We present a tool that combines two main trends of knowledge base refinement. The first is the construction of interactive knowledge acquisition tools and the second is the development of machine learning methods that automate this procedure. The tool presented here is interactive and gives experts the ability to evaluate an expert system and provide their own diagnoses on specific problems, when the expert system behaves erroneously. We also present a database scheme that supports the collection of specific instances. The second aspect of the tool is that knowledge base refinement and machine learning methods can be applied to the database, in order to automate the procedure refining the knowledge base. In this paper we examine the application of inductive learning algorithms within the proposed framework. Our main goal is to encourage the experts to evaluate expert systems and to introduce new knowledge, based on their experience.     

A case-based procurement advisory system for construction

The selection and use of an appropriate procurement system are fundamental to the success of a construction project. However, the procurement selection process involves the analysis of complex and dynamic criteria such as cost certainty, time certainty, speed, flexibility, etc. Procurement selection is, therefore, plagued with uncertainty and vagueness that is difficult to be represented by a generalized set of rules. In reality, decisions in procurement selection are usually derived from intuition and past experience. Case-based reasoning (CBR) appears to be an appropriate approach to meet the requirements of the procurement selection process because of the value of experiential knowledge. This paper reviews the practicality and suitability of a CBR approach for procurement selection through the development of a prototype case-based procurement advisory system. In this prototype system, procurement selection cases are represented by a set of attributes elicited from experienced procurement experts. The system is powered by a fuzzy similarity retrieval mechanism, which gives a greater accuracy than the normal similarity retrieval process. The results indicate that the CBR approach can suitably model the characteristics of construction procurement selection, and provide an indication of potential outcomes to any apparently suitable procurement methods.     

SEDAF: AN INTELLIGENT EDUCATIONAL SYSTEM FOR MATHEMATICS

The field of artificial intelligence and education, in which AI techniques and methodologies are used to build sophisticated intelligent educational systems, is developing rapidly. In this paper we present an intelligent educational system for teaching high school and college students how to analyze and draw graphs of mathematical functions. The system, named SEDAF, has been developed in a knowledge engineering environment and runs on a Lisp-machine workstation. We illustrate the various modules constituting SEDAF: the user interface; an expert module, capable of solving problems in the subject domain; a diagnosis module, which points out possible reasons for students' errors; a student modeling module, capable of building an explicit representation of the learning status of the student; and a remedial subsystem, called a therapy module, constituted by means-ends tutorial rules that execute teaching actions on the base of the status of the student model. The goal of the presentation is to stress the innovative aspects of the architecture of SEDAF, in particular the use of metalevel knowledge to embed in the system the teaching expertise that allows the system to personalize its behavior to the specific student and to pursue a didactic plan.     

Data Mining via Discretization, Generalization and Rough Set Feature Selection

We present a data mining method which integrates discretization, generalization and rough set feature selection. Our method reduces the data horizontally and vertically. In the first phase, discretization and generalization are integrated. Numeric attributes are discretized into a few intervals. The primitive values of symbolic attributes are replaced by high level concepts and some obvious superfluous or irrelevant symbolic attributes are also eliminated. The horizontal reduction is done by merging identical tuples after substituting an attribute value by its higher level value in a pre- defined concept hierarchy for symbolic attributes, or the discretization of continuous (or numeric) attributes. This phase greatly decreases the number of tuples we consider further in the database(s). In the second phase, a novel context- sensitive feature merit measure is used to rank features, a subset of relevant attributes is chosen, based on rough set theory and the merit values of the features. A reduced table is obtained by removing those attributes which are not in the relevant attributes subset and the data set is further reduced vertically without changing the interdependence relationships between the classes and the attributes. Finally, the tuples in the reduced relation are transformed into different knowledge rules based on different knowledge discovery algorithms. Based on these principles, a prototype knowledge discovery system DBROUGH-II has been constructed by integrating discretization, generalization, rough set feature selection and a variety of data mining algorithms. Tests on a telecommunication customer data warehouse demonstrates that different kinds of knowledge rules, such as characteristic rules, discriminant rules, maximal generalized classification rules, and data evolution regularities, can be discovered efficiently and effectively.     

Feature-based intelligent system for steam simulation using computational fluid dynamics

In the development of products involving fluids, computational fluid dynamics (CFD) has been increasingly applied to investigate the flow associated with various product operating conditions or product designs. The batch simulation is usually conducted when CFD is heavily used, which is not able to respond to the changes in flow regime when the fluid domain changes. In order to overcome this defect, a rule-based intelligent CFD simulation system for steam simulation is proposed to analyze the specific product design and generate the corresponding robust simulation model with accurate results. The rules used in the system are based on physical knowledge and CFD best practices which make this system easy to be applied in other application scenarios by changing the relevant knowledge base. Fluid physics features and dynamic physics features are used to model the intelligent functions of the system. Incorporating CAE boundary features, the CFD analysis view is fulfilled, which maintains the information consistency in a multi-view feature modeling environment. The prototype software tool is developed by Python 3 with separated logics and settings. The effectiveness of the proposed system is proven by the case study of a disk-type gate valve and a pipe reducer in a piping system.     

Development of an expert system for optimizing natural gas pipeline operations

This paper presents an expert system as a decision support tool to optimize natural gas pipeline operations. A natural gas pipeline control system is a controlling system that involves many complicated operating processes. Since a dispatcher (who operates the system) might not be able to handle all of his or her tasks consistently, an expert system has been developed for optimizing the operations by providing consistent, fast and reliable decision support to the dispatcher. Consequently, inconsistency in the dispatcher's performance can be minimized. To build an expert system, the knowledge from an experienced dispatcher, who is familiar with the process in this controlling system is acquired and that knowledge has been implemented as rules in the knowledge base of the expert system. When this expert system has been validated by gas pipeline experts, it can help inexperienced dispatchers to operate the processes more effectively. The expert system is implemented on the real-time expert system shell G2 (trademark of Gensym Corp. of USA). The system also consists of a user interface that helps dispatchers visualize system conditions.