• Title, Summary, Keyword: Rule-Based Model

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The method of using database technology to process rules of Rule-Based System

  • Zheng, Baowei;Yeo, Jeong-Mo
    • Journal of information and communication convergence engineering
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    • v.8 no.1
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    • pp.89-94
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    • 2010
  • The most important of rule-base system is the knowledge base that determines the power of rule-base system. The important form of this knowledge is how to descript kinds of rules. The Rule-Base System (RBS) has been using in many field that need reflect quickly change of business rules in management system. As far, when develop the Rule-Based System, we must make a rule engine with a general language. There are three disadvantage of in this developed method. First, while there are many data that must be processed in the system, the speed of processing data will become very slow so that we cannot accept it. Second, we cannot change the current system to make it adaptive to changes of business rules as quickly as possible. Third, large data make the rule engine become very complex. Therefore, in this paper, we propose the two important methods of raising efficiency of Rule-Base System. The first method refers to using the Relational database technology to process the rules of the Rule-Base System, the second method refers to a algorithm of according to Quine McCluskey formula compress the rows of rule table. Because the expressive languages of rule are still remaining many problems, we will introduce a new expressive language, which is Rule-Base Data Model short as RBDM in this paper.

Development of a Rule-Based Inference Model for Human Sensibility Engineering System

  • Yang Sun-Mo;Ahn Beumjun;Seo Kwang-Kyu
    • Journal of Mechanical Science and Technology
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    • v.19 no.3
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    • pp.743-755
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    • 2005
  • Human Sensibility Engineering System (HSES) has been applied to product development for customer's satisfaction based on ergonomic technology. The system is composed of three parts such as human sensibility analysis, inference mechanism, and presentation technologies. Inference mechanism translating human sensibility into design elements plays an important role in the HSES. In this paper, we propose a rule-based inference model for HSES. The rule-based inference model is composed of five rules and two inference approaches. Each of these rules reasons the design elements for selected human sensibility words with the decision variables from regression analysis in terms of forward inference. These results are evaluated by means of backward inference. By comparing the evaluation results, the inference model decides on product design elements which are closer to the customer's feeling and emotion. Finally, simulation results are tested statistically in order to ascertain the validity of the model.

Extraction of Fuzzy Rules with Importance for Classifier Design

  • Pal, Kuhu
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.725-730
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    • 1998
  • Recently we extended the fuzzy model for rule based systems incorporating an importance factor for each rule. The model permits for both unrestricted as well as non-negative importance factors. We use this extended model to design a fuzzy rule based classifier system which uses both the firing strength of the rule and the importance factor to decide the class label. The effectiveness of the scheme is established using several data sets.

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Laplace-Metropolis Algorithm for Variable Selection in Multinomial Logit Model (Laplace-Metropolis알고리즘에 의한 다항로짓모형의 변수선택에 관한 연구)

  • 김혜중;이애경
    • Journal of the Korean Society for Quality Management
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    • v.29 no.1
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    • pp.11-23
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    • 2001
  • This paper is concerned with suggesting a Bayesian method for variable selection in multinomial logit model. It is based upon an optimal rule suggested by use of Bayes rule which minimizes a risk induced by selecting the multinomial logit model. The rule is to find a subset of variables that maximizes the marginal likelihood of the model. We also propose a Laplace-Metropolis algorithm intended to suggest a simple method forestimating the marginal likelihood of the model. Based upon two examples, artificial data and empirical data examples, the Bayesian method is illustrated and its efficiency is examined.

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Rule-Based Framework for user level delegation model in Role Based Access Control (역할기반 접근제어에서의 사용자 수준의 위임기법에 대한 Rule-Based Framework)

  • 박종화
    • The Journal of Information Technology
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    • v.4 no.3
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    • pp.139-154
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    • 2001
  • In current role-based systems, security officers handle assignments of users to roles. This may increase management efforts in a distributed environment because of the continuous involvement from security officers. The technology of role-based delegation provides a means for implementing RBAC in a distributed environment with empowerment of individual users. The basic idea behind a role-based delegation is that users themselves may delegate role authorities to other users to carry out some functions on behalf of the former. This paper presents a rule-based framework for user-level delegation model in which a user can delegate role authority by creating new delegation roles. Also, a rule-based language for specifying and enforcing the policies is introduced.

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User Satisfaction Models Based on a Fuzzy Rule-Based Modeling Approach (퍼지 규칙 기반 모델링 기법을 이용한 감성 만족도 모델 개발)

  • Park, Jungchul;Han, Sung H.
    • Journal of Korean Institute of Industrial Engineers
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    • v.28 no.3
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    • pp.331-343
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    • 2002
  • This paper proposes a fuzzy rule-based model as a means to build usability models between emotional satisfaction and design variables of consumer products. Based on a subtractive clustering algorithm, this model obtains partially overlapping rules from existing data and builds multiple local models each of which has a form of a linear regression equation. The best subset procedure and cross validation technique are used to select appropriate input variables. The proposed technique was applied to the modeling of luxuriousness, balance, and attractiveness of office chairs. For comparison, regression models were built on the same data in two different ways; one using only potentially important variables selected by the design experts, and the other using all the design variables available. The results showed that the fuzzy rule-based model had a great benefit in terms of the number of variables included in the model. They also turned out to be adequate for predicting the usability of a new product. Better yet, the information on the product classes and their satisfaction levels can be obtained by interpreting the rules. The models, when combined with the information from the regression models, are expected to help the designers gain valuable insights in designing a new product.

A Representation of Uncertain Knowledge of Rule Base Reasoning and Case Base Reasoning (규칙베이스와 사례베이스 추론의 불확실한 지식의 표현)

  • Chung, Gu-Bum;Roh, Eun-Young;Chung, Hawn-Mook
    • Journal of the Korean Institute of Intelligent Systems
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    • v.21 no.2
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    • pp.165-170
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    • 2011
  • It is expected that the cooperation between rule-based reasoning and case-based reasoning gives us an efficient approach for flexible reasoning. In this paper, we present an integrated model of rule-base reasoning and case-base reasoning using the MVL automata model. In addition, we introduce how to handle the uncertainty in the integrated model.

A CAD Model Healing System with Rule-based Expert System (전문가시스템을 이용한 CAD 모델 수정 시스템)

  • Han Soon-Hung;Cheon Sang-Uk;Yang Jeong-Sam
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.30 no.3
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    • pp.219-230
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    • 2006
  • Digital CAD models are one of the most important assets the manufacturer holds. The trend toward concurrent engineering and outsourcing in the distributed development and manufacturing environment has elevated the importance of high quality CAD model and its efficient exchange. But designers have spent a great deal of their time repairing CAD model errors. Most of those poor quality models may be due to designer errors caused by poor or incorrect CAD data generation practices. In this paper, we propose a rule-based approach for healing CAD model errors. The proposed approach focuses on the design history data representation from a commercial CAD model, and the procedural method for building knowledge base to heal CAD model. Through the use of rule-based approach, a CAD model healing system can be implemented, and experiments are carried out on automobile part models.

A Model-Based Tuning Rule of the PID Controller (PID 제어기의 모델기반 동조규칙)

  • 김도응;신명호;권봉재;유성호;박승수;진강규
    • Proceedings of the Korean Society of Marine Engineers Conference
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    • pp.261-266
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    • 2002
  • In this Paper, we Propose model-based tuning rules of the PID controller incorporating with genetic algorithms. Three sets of optimal PID parameters for step set-point tracking are obtained based on the first-order time delay model of plants and a genetic algorithm which minimizes performance indices(IAE, ISE and ITAE). Then tuning rules are obtained using the tuned parameter sets, potential rule models and a genetic algorithm. Simulation is carried out to verify the effectiveness of the proposed rules.

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Network Intrusion Detection Based on Directed Acyclic Graph and Belief Rule Base

  • Zhang, Bang-Cheng;Hu, Guan-Yu;Zhou, Zhi-Jie;Zhang, You-Min;Qiao, Pei-Li;Chang, Lei-Lei
    • ETRI Journal
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    • v.39 no.4
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    • pp.592-604
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    • 2017
  • Intrusion detection is very important for network situation awareness. While a few methods have been proposed to detect network intrusion, they cannot directly and effectively utilize semi-quantitative information consisting of expert knowledge and quantitative data. Hence, this paper proposes a new detection model based on a directed acyclic graph (DAG) and a belief rule base (BRB). In the proposed model, called DAG-BRB, the DAG is employed to construct a multi-layered BRB model that can avoid explosion of combinations of rule number because of a large number of types of intrusion. To obtain the optimal parameters of the DAG-BRB model, an improved constraint covariance matrix adaption evolution strategy (CMA-ES) is developed that can effectively solve the constraint problem in the BRB. A case study was used to test the efficiency of the proposed DAG-BRB. The results showed that compared with other detection models, the DAG-BRB model has a higher detection rate and can be used in real networks.