• IE interfaces
• /
• v.3 no.2
• /
• pp.1-11
• /
• 1990

In this study, we will present a knowledge-based consulting system, called THYCONS, for diagnosing thyroid disorders. It has been developed to make the knowledge and expertise of the human expert more widely available, therefore achieving a high-quality diagnosis. Frames will be used to represent the medical knowledge of thyroid disorders, and several rules are attached in each slot of a frame. The uncertainty of diagnostic processes is manipulated by the subjective Bayesian method under the assumption that the pieces of evidence are conditionally independent. Searching for the group of diagnostic tests to be carried out and their optimum sequences will be established in order to infer a more correct diagnosis on the basis of maximum information gain with cost and time restrictions. Additionally. differential diagnosis will be carried out based on the information gained.

• Proceedings of the IEEK Conference
• /
• summer
• /
• pp.87-91
• /
• 2004

This research presents distributed database and knowledge-base modeling approach for intelligent product design system. The product design information in this study is described by a collection of rules and design knowledge that are utilized according to the product development procedures. In this work, a network-based architecture has been developed to enable dispersed designers to simultaneously accomplish remote design tasks. A client/server communication diagram has also been proposed to facilitate consistent primary information modeling for multi-user access and reuse of designed results. An intelligent product design system has been studied with the concepts of distributed database and network-based architecture in order to support concurrent engineering design and automatic design part assembly. The system provides the capability of composing new designs from proper design elements stored in the database and knowledge-base. The distributed intelligent product design is applied to the design of an automobile part as an example.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.2
• /
• pp.118-130
• /
• 1996

Fuzzy inference is very useful in expressing ambiguous problems quantitatively and solving them. But like the most of the knowledge based inference systems. It has many difficulties in constructing rules and no learning capability is available. In this paper, we proposed a fuzzy inference system based on fuzy associative memory to solve such problems. The inference system proposed in this paper is mainly composed of learning phase and inference phase. In the learning phase, the system initializes it's basic structure by determining fuzzy membership functions, and constructs fuzzy rules in the form of weights using learning function of fuzzy associative memory. In the inference phase, the system conducts actual inference using the constructed fuzzy rules. We applied the fuzzy inference system proposed in this paper to a pattern classification problem and show the results in the experiment.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.5
• /
• pp.1289-1296
• /
• 1997

To build up a knowledge-based system in an Artifical Inerligence System, selecting an appropriate set of rules is one of the key provlems. In this paper, we discuss a new method for exteacting fuzzy rules diredtly from fuzzy membdrchip function dat for pattern classifcation. The fuzzy rules with variable fuzzy recions are defined by sharing fuzzy space in fuzzy grid.Tehse rules are extracted form memberchop function. Them, optimal input vari-ables for the rules are determined using the number of extracted rules as a criterion. The method is compared with neural networks using Ishibuchi. Finally, in order to demonstrate the cffectiveness of the present method, simulation results are shown.

• The Transactions of the Korea Information Processing Society
• /
• v.5 no.4
• /
• pp.942-950
• /
• 1998

Extraction of knowledge, especially in the form of rules, from raw data is very important in data mining, the aim of which is to help users who feel the lack of knowledge in spite of the abundance of data. Logic minimization tools are ones which derive optimized knowledge given ON set and DC set. First, the parallel expansion scheme of logic minimization is extracted and used to obtain intial knowledge to get final rules, which are successfully applicable to real world data. The prototype system based on this new approach has been experimented with real world data to show that it is as practical as conventional long studied decision tree methods like C4.5 system.

• Journal of the Korea Society of Computer and Information
• /
• v.1 no.1
• /
• pp.159-170
• /
• 1996

In this paper, we propose a generating method for the optimal rules for the nonlinear control system using evolution algorithms and fuzzy controller. With the aid of evolution algorithms optimal rules of fuzzy logic system can be automatic designed without human expert's priori experience and. knowledge. and ran be intelligent control. The approachpresented here generating rules by self-tuning the parameters of membership functions and searchs the optimal control rules based on a fitness value which Is tile defined performance criterion. Computer simulations demonstrates the usefulness of the proposed method In non -linear systems.

• Proceedings of the KIEE Conference
• /
• 2000.07d
• /
• pp.2377-2379
• /
• 2000

Design of Fuzzy cotroller consists of intuition of human expert, and any other information about how to control system, they translated into a set of rules. If the rules adequately control the system, the design work is done well. If the rules are inadequate, the designer must modify the rules. Through this procedure, the system can be controlled. In this paper, we designed simply a fuzzy controller based on human knowledge, but it has errors showing some vibrations. So we updated the optimal parameters of fuzzy controller using Recursive least square algorithm.

• Journal of Korea Multimedia Society
• /
• v.7 no.4
• /
• pp.559-566
• /
• 2004

In general, the certainty factors of the fuzzy production rules and the certainty factors of fuzzy propositions appearing in the rules are represented by real values between zero and one. If it can allow the certainty factors of the fuzzy production rules and the certainty factors of fuzzy propositions to be represented by interval -valued fuzzy sets, then it can allow the reasoning of rule-based systems to perform fuzzy reasoning in more flexible manner. This paper presents fuzzy Petri nets and proposes an interval-valued fuzzy backward reasoning algorithm for rule-based systems based on fuzzy Petri nets Fuzzy Petri nets model the fuzzy production rules in the knowledge base of a rule-based system, where the certainty factors of the fuzzy propositions appearing in the fuzzy production rules and the certainty factors of the rules are represented by interval-valued fuzzy sets. The algorithm we proposed generates the backward reasoning path from the goal node to the initial nodes and then evaluates the certainty factor of the goal node. The proposed interval-valued fuzzy backward reasoning algorithm can allow the rule-based systems to perform fuzzy backward reasoning in a more flexible and human-like manner.

• Journal of KIISE:Software and Applications
• /
• v.31 no.5
• /
• pp.625-631
• /
• 2004

In general, the certainty factors of the fuzzy production rules and the certainty factors of fuzzy Propositions appearing in the rules are represented by real values between zero and one. If it can allow the certainty factors of the fuzzy production rules and the certainty factors of fuzzy propositions to be represented by interval-valued fuzzy sets, then it can allow the reasoning of rule-based systems to perform fuzzy reasoning in more flexible manner(15). This paper presents a fuzzy Petri nets and proposes an interval-valued fuzzy reasoning algorithm for rule-based systems based on fuzzy Petri nets. Fuzzy Petri nets model the fuzzy production rules in the knowledge base of a rule-based system, where the certainty factors of the fuzzy Propositions appearing in the furry production rules and the certainty factors of the rules are represented by interval-valued fuzzy sets. The proposed interval-valued fuzzy set reasoning algorithm can allow the rule-based systems to perform fuzzy reasoning in a more flexible manner.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.40 no.8
• /
• pp.731-740
• /
• 1991

This paper presents a knowledge based system to solve reactive power/voltage control problem in a power system. The methods to reduce inference time are proposed in inferring the solution of problem in the knowledge base which consists of heuristic rules and inowledge of experts. A set of indices drawn from the heuristic knowledge on the power system is utilized to make up for the defect of existing knowledge based systems which determine both the location and the amount of reactive power compensation devices. The concept of set of indices developed in this paper makes it possible to infer the amount of reactive power source only since the bus order list representing priority for the location of reactive power compensator to be switched on can be determined in advance. From the fact that there exists a relationship between the system voltage pattern and the reactive power pattern in operation, the pattern recognition technique is introduced to reduce the inference time in solving the severe voltage problem. To demonstrate the usefulness of the proposed knowledge based system, the IEEE 30 bus system is chosen as a sample system. The results of case study are also presented.