• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.40 no.5
• /
• pp.250-258
• /
• 2003

In this paper, an explanation regarding on-line identification of a fuzzy system is presented. The fuzzy system to be identified is assumed to be in the type of singleton consequent parts and be represented by a linear combination of fuzzy basis functions. For on-line identification, squared-cosine membership function is introduced to reduce the number of parameters to be identified and make the system consistent and differentiable. Then the parameters of the fuzzy system are identified on-line by the gradient search method and Extended Kalman Filter. Finally, a computer simulation is peformed to illustrate the validity of the suggested algorithms.

• Information Systems Review
• /
• v.23 no.1
• /
• pp.1-22
• /
• 2021

This study aims to analyze online game user's game items transacting behaviors for the two game genres such as MMORPG and sports game. For the sake of conducting the analysis, we adopted a fuzzy logic agent-based modeling. In the online game fields, game items transactions are crucial to game company's profitability. However, there are lack of previous studies investigating the online game user's game items transacting activities. Since many factors need to be addressed in a complicated way, ABM (agent-based modeling) simulation mechanism is adopted. Besides, a fuzzy logic is also considered due to the fact that a number of uncertainties and ambiguities exist with respect to online game user's complex behaviors in transacting game items. Simulation results from applying the fuzzy logic ABM method revealed that MMORPG game users are motivated to pay expensive price for high-performance game items, while sports game users tend to transact game items within a reasonable price range. We could conclude that the proposed fuzzy logic ABM simulation mechanism proved to be very useful in organizing an effective strategy for online game items management and customers retention.

• Proceedings of the KAIS Fall Conference
• /
• 2003.06a
• /
• pp.193-195
• /
• 2003

최근 산업의 핵심 에너지원을 공급하고 있는 전력회사의 운영에서, 계통 사고시 대량의 경보 신호 발생으로 인하여 운전원의 혼란을 가져오게 되어, 사고후 복구시간의 지연을 초래하는 문제로 인한 해당산업체의 손실이 대형화하는 추세이다. 본 논문에서는 이와 같은 문제를 해결하는 방안으로 사고시 보호시스템의 순차정보를 이용하여 단시간에 고장진단을 수행함으로서, 고장의 원인 파악과 정확한 고장발생지점 정보를 운전원에게 제공할 수 있는 방법을 제안하고 있다. 고장진단 기법은 고장 발생의 불확실성을 고려한 보호시스템의 모델링 방법과 퍼지 페트리네트 기법을 개발하여 적용하였다. 본 연구에서 개발한 방법을 사례연구를 통하여 모델 계통에 적응하고 그 유효성 여부를 확인한 결과 만족할 만한 성과를 얻을 수 있었다. 특히 보호시스템의 오동작이나 부동작 둥의 불확실한 정보를 처리하는 데 본 연구에서 개발한 퍼지 페트리네트 기법이 탁월한 성능을 발휘하므로 실제의 대형 전력계통에 적용 가능성을 확인할 수 있었다. 또한 본 방법은 SCADA로부터 전송되는 실시간 데이터의 온라인 처리도 가능하므로 그 유용성은 아주 높다고 볼 수 있다.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.10
• /
• pp.1293-1302
• /
• 1999

In this paper, an on-line process scheme is presented for implementation of a intelligent on-line modeling of nonlinear complex system. The proposed on-line process scheme is composed of FNN-based model algorithm and PLC-based simulator, Here, an adaptive fuzzy-neural networks and HCM(Hard C-Means) clustering method are used as an intelligent identification algorithm for on-line modeling. The adaptive fuzzy-neural networks consists of two distinct modifiable sturctures such as the premise and the consequence part. The parameters of two structures are adapted by a combined hybrid learning algorithm of gradient decent method and least square method. Also we design an interface S/W between PLC(Proguammable Logic Controller) and main PC computer, and construct a monitoring and control simulator for real process system. Accordingly the on-line identification algorithm and interface S/W are used to obtain the on-line FNN model structure and to accomplish the on-line modeling. And using some I/O data gathered partly in the field(plant), computer simulation is carried out to evaluate the performance of FNN model structure generated by the on-line identification algorithm. This simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy and feasibility than other works achieved previously.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.8 no.3
• /
• pp.80-87
• /
• 1998

This paper presents and online scheme for fuzzy modelling of nonlinear systems, based on the model adjustment technique and the genetic algorithm technique. The fuzzy model is characterized by fuzzy "if-then" rules which represent locally linear input-output relations whose consequence parts are defined as subsystems of a nonlinear sysem. The discrete-time model for each subsystem is obtained to deal with initalization and unmeasurable signal problems in online estimation and the final output of the fuzzy model is computed from the outputs of the discrete-time models. Then, the parameters of both the premise and consequence parts of the fuzzy model are adjusted by a genetic algorithm. A set of simulation works is carried out to demonstrate the effectiveness of the proposed method.ed method.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.8 no.6
• /
• pp.37-47
• /
• 1998

This paper proposes a controller design method using the evolutionary computation algorithm and the fuzzy logic to control the wafer temperature in rapid thermal processing. First, we design the feedforward static controller to provide the control powers of the lamps for the given steady state temperature. Second, the feedforward dynamic controller is designed for the additional control powers to achieve a given transient response. These feedforward controllers are implemented by using the fuzzy logic to act as a global nonlinear controller over a wide range of operating points. The parameters of these controllers are optimized by using the evolutionary computation algorithm so that it can be used when the mathematical model is not available. In addition, the feedback error controller is introduced to compensate the feedforward controllers when there exist disturbances and modeling errors. The gain of feedback error controller is also obtained by the evolutionary computation algorithm. Through simulations, we verify the proposed control system can give a satisfactory performance.