• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.725-730
• /
• 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.

• Journal of Advanced Marine Engineering and Technology
• /
• v.28 no.2
• /
• pp.228-236
• /
• 2004

In this paper. we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feed forward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand. feed forward neural networks provide salient features. such as learning and parallelism. In the proposed neuro-fuzzy controller. the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error back propagation algorithm as a learning rule. while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally. the effectiveness of the proposed controller is verified through computer simulation for an inverted pole system.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.417-422
• /
• 1998

This paper presents a method of object classification from dynamic image based on fuzzy inference algorithm which is suitable for low speed such as, conveyor, uninhabited transportation. At first, by using feature parameters of moving object, fuzzy if - then rule that can be able to adapt the wide variety of surroundings is developed. Secondly, implication function for fuzzy inference are compared with respect the proposed algorithm. Simulation results are presented to testify the performance and applicability of the proposed system.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.41 no.10
• /
• pp.69-77
• /
• 2004

In this thesis, we created a Fuzzy rule in a Fuzzy logic that are fuzzy logic which is composed of linguistic rules and Fuzzy inference engine for effective traffic control in ATM networks. The parameters of the Fuzzy rules are adapted to minimize the given performance index in both cases. In other words, the difuzzification value controls the service rate in the server to total traffic arrival ratio and buffer occupancy ratio using fuzzy set theory for traffic connected after reasoning. Also, show experiment result about rule by MATLAB6.5 and on-line bulid-up to verify validity of created Fuzzy rule. As a result, we can verify that service ratio in server is efficiently controlled by the total traffic arrival ratio and buffer occupancy ratio.

• 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.

• Proceedings of the KIEE Conference
• /
• 2002.11c
• /
• pp.333-336
• /
• 2002

In many cases, fuzzy modeling has a defect that the design procedure cannot be theoretically justified. To overcome this difficulty, we suggest a new design method for fuzzy model by combining genetic algorithm(GA) and mush set theory. GA, which has the advantages is optimization, and rule base. However, it is some what time consuming, so are introduce rough set theory to the rule reduction procedure. As a result, the decrease of learning time and the considerable rate of rule reduction is achieved without loss of useful information. The preposed algorithm is composed of three stages; First stage is quasi-optimization of fuzzy model using GA(coarse tuning). Next the obtained rule base is reduced by rough set concept(rule reduction). Finally we perform re-optimization of the membership functions by GA(fine tuning). To check the effectiveness of the suggested algorithm, examples for time series prediction are examined.

• Journal of the Korea Society of Computer and Information
• /
• v.6 no.2
• /
• pp.48-57
• /
• 2001

In a fuzzy connectionist expert system(FCES), the knowledge base can be constructed of neural logic networks to represent fuzzy rules and their relationship, We call it fuzzy rule inference network. To find out the belief value of a conclusion, the traditional inference strategy in a FCES will back-propagate from a rule term of the conclusion and follow through the entire network sequentially This sequential search strategy is very inefficient. In this paper, to improve the above search strategy, we proposed fuzzy rule inference rule used in a FCES was modified. The proposed adaptive search strategy in fuzzy rule inference network searches the network according to the search priorities.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.36C no.8
• /
• pp.54-61
• /
• 1999

In this paper, we apply one of modified Genetic Algorithms, a Multi-population Genetic Algorithm(MGA) that improves the genetic diversity to determine the fuzzy rule base and the shape of membership functions. The generation of the fuzzy rule base for fuzzy control, generally, depends on expert's experience. We suggest a new evaluation function to optimize fuzzy rule base. Simulation shows that the proposed method has good result.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.28B no.7
• /
• pp.557-565
• /
• 1991

The activated sludge process is a commonly used method for terating sewage and waste waters. The process is chatacterized by a lack of measurement instrumentations and control goals that are not always clear and not well understood. In such process, fuzzy control concept may be able to be adapted, do this paper presents a design method for fuzzy controller based on a selected sub-rule set from the total rule set and a multivariable fuzzy reasoning algorithms. In order to achievesystematic and efficient control of the activated sludge process under a great deal of disiutbances and a variety of perfotmance characteristics, a top-level rule-based fuzzy controller os proposed which provises lower-controllers with the suitable set-points according tothe onput-output states of the process.