• Journal of Internet of Things and Convergence
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• v.4 no.1
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• pp.1-5
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• 2018

This paper is concerned with the modeling and identification of time series data corrupted by noise. As modeling techniques, nonsingleton fuzzy logic system (NFLS) is employed for the modeling of corrupted time series. Main characteristic of the NFLS is a fuzzy system whose inputs are modeled as fuzzy number. So the NFLS is especially useful in cases where the available training data or the input data to the fuzzy logic system are corrupted by noise. Simulation results of the Mackey-Glass time series data will be demonstrated to show the performance of the modeling methods. As a result, NFLS does a much better job of modeling noisy time series data than does a traditional Mamdani FLS.

• The Transactions of the Korean Institute of Power Electronics
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• v.2 no.4
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• pp.45-55
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• 1997

According to the increase of factory-automation in the field of production, the importance of autonomous guided vehicle's(AGV) role is also increased. The study about an active and effective controller which can flexibly prepare for the changeable circumstance is in progressed. For this study, the research about action base system to evolve by itself is also being actively considered. In this paper, we composed an active and effective AGV fuzzy controller to be able to do self-organization. For composing it, we tuned suboptimally membership function using genetic algorithm(GA) and improved the control efficiency by the self-correction and generating the control rules. Self-organizing controlled(S0C) fuzzy controller proposed in the paper is capable of self-organizing by using the characteristics of fuzzy controller and genetic algorithm. It intuitionally controls AGV and easily adapts to the circumstance.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2251-2260
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• 2021

The motion control of the divertor maintenance system of the China Fusion Engineering Test Reactor (CFETR) was studied in this paper, in which CFETR Multi-Functional Maintenance Platform (MFMP) was simplified as a parallel robot for the convenience of theoretical analysis. In order to design the motion controller of parallel robot, the kinematics analysis of parallel robot was carried out. After that, the dynamic modeling of the hydraulic system was built. As the large variation of heavy payload on MFMP and highly nonlinearity of the system, A Fuzzy-PID controller was built for self-tuning PID controller parameters by using Fuzzy system to achieve better performance. In order to test the feasibility of the Fuzzy-PID controller, the simulation model of the system was built in Simulink. The results have showed that Fuzzy-PID controller can significantly reduce the angular error of the moving platform and provide the stable motion for transferring the divertor.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.96-98
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• 2006

축산폐수는 축사가 대부분 상수원보다 상류지역에 산재하고 있어 이를 효과적으로 관리하기 어려우나, 연속 회분식 반응기(Sequencing Batch Reactor, SBR)는 장치가 간단하고 경제성이 우수하여 축산폐수처리에서 효율적으로 적용될 수 있다. 본 연구에서는 DO(Dissolved Oxygen)과 ORP(Oxidation-Reduction Potential)을 이용하여 지식기반 고장진단 시스템을 제안하였다. 실시간으로 얻어진 ORP, DO값들을 전처리하여, [ORP], [DO]외에 [ORP DO]합성data와 ORP, DO의 특징백터의 합에서 얻어진 fusion data의 총 4개의 data set을 이용하여 각각에 대한 진단과 분류성능을 검토하였다. 이 값을 이용하여 FCM (fuzzy C-mean) 클러스터링 한 후, K-PCA과 LDA로 차원축소시켜 특징백터를 추출하였다. 그리고 Hamming distance로 test data와 특징백터의 거리를 계산하여 각 class를 F1에서 F8까지 분류하였다. 그 결과 데이터를 그대로 이용하는 것 보다 차분데이터형태로 이용하는 것이 우수했으며 그 중 fusion 데이터의 결과가 다른 것들보다 향상된 결과를 보였다. 그리고 K-PCA와 LDA를 결합한 결과가 다른 방법에 비해 우수한 결과를 보였으며 fusion method를 이용한 최고인식율은 98.02%를 나타내었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.157-163
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• 2005

In this paper, we present the multimodal system based on the fusion of two user-friendly biometric modalities: Iris and Face. In order to reach robust identification and verification we are going to combine two different biometric features. we specifically apply 2-D discrete wavelet transform to extract the feature sets of low dimensionality from iris and face. And then to obtain Reduced Joint Feature Vector(RJFV) from these feature sets, Direct Linear Discriminant Analysis (DLDA) is used in our multimodal system. In addition, the Synergetic Neural Network(SNN) is used to obtain matching score of the preprocessed data. This system can operate in two modes: to identify a particular person or to verify a person's claimed identity. Our results for both cases show that the proposed method leads to a reliable person authentication system.

• Proceedings of the KSEEG Conference
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• 1999.04a
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• pp.147-147
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• 1999

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.11
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• pp.120-129
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• 1992

In this paper we propose a fuzzy-neural network(FNN) which includes both advantages of the fuzzy logic and the neural network. The basic idea of the FNN is to realize the fuzzy rule-base and the process of reasoning by neural network and to make the corresponding parameters be expressed by the connection weights of neural network. After constructing the FNN, a novel controller consisting of a conventional P-controller and a FNN is explained. In this control scheme, the rule-base of a FNN are automatically generated by error back-propagation algorithm. Also the parallel connection of the P-controller and the FNN can guarantee the stability of a plant at initial stage before the rules are completely created. Finally the effectiveness of the proposed strategy will be verified by computer simulations using a 2 degree of freedom robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2372-2377
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• 2003

Nonlinear dynamic system exist widely in many types of systems such as chemical processes, biomedical processes, and the main steam temperature control system of the thermal power plant. Up to the present time, PID Controllers have been used to operate these systems. However, it is very difficult to achieve an optimal PID gain with no experience, because of the interaction between loops and gain of the PID controller has to be manually tuned by trial and error. This paper suggests control approaches by immune fuzzy for the nonlinear control system inverted pendulum, through computer simulation. This paper defines relationship state variables $x,{\dot{x}},{\theta},\dot{\theta}$ using immune fuzzy and applied its results to stability.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.296-301
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• 1998

The Rough Set theory suggested by Pawlak in 1982 has been useful in AI, machine learning, knowledge acquisition, knowledge discovery from databases, expert system, inductive reasoning. etc. The main advantages of rough set are that it does not need any preliminary or additional information about data and reduce the superfluous informations. but it is a significant disadvantage in the real application that the inference result form is not the real control value but the divided disjoint interval attribute. In order to overcome this difficulty, we will propose approach in which Rough set theory and Neuro-fuzzy fusion are combined to obtain the optimal rule base from lots of input/output datum. These results are applied to the rule construction for infering the temperatures of refrigerator's specified points.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.331-333
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• 1994

A learning method for induction motor trajectory using neuro-fuzzy networks (NFN) based on fusion of fuzzy logic theory and neural networks is proposed. The premise and consequent parameters of the NFN affecting the controllers performances are modified during the learning stages by the proposed learning method to implement an optimal controller only with pre-determined target trajectory and the least amount of knowledge about an induction motor. The induction motor position control system is simulated to verify the effectiveness of the learned NF controller(NFC). The simulation results shows that the proposed learning method has good dynamic performance and small steady state error.