• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1118-1120
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• 1993

Fuzzy logic places a considerable burden on an inference engine for applications such as control or approximate reasoning. Various neural network architectures have been proposed to deal with the computational task, and yet, maintain flexibility in the desired traits of the final system. Recently, we introduced a trainable network architecture whose nodes implement weighted Yager additive hybrid operators for fuzzy logic inference in an approximate reasoning setting. In this paper we examine the utility of such networks for control situations. We show that they are capable of learning control functions which are piece-wise monotonic in each of the variables. The learning ability is demonstrated through an example.

• 대한전기학회논문지:전력기술부문A
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• 제48권4호
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• pp.444-451
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• 1999

A diagnostic rule-base design method for enhancing fault detection and isolation performance of multiple obsever based fault detection isolation schemes (FIDS) is presented. The diagnostic rule-base has a hierarchical framework to perform detection and isolation of faults of interest, and diagnosis of process faults. The decision unit comprises a rule base and a fuzzy inference engine and removes some difficulties of conventional decision unit which includes crisp logic with threshold values. Emphasis is placed on the design and evaluation methods of the diagnostic rult-base. The suggested scheme is applied to the FDIS design for a DC motor driven centrifugal pump system.

• Transactions on Control, Automation and Systems Engineering
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• 제3권3호
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• pp.170-175
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• 2001

Q-learning is a kind of reinforcement learning where the agent solves the given task based on rewards received from the environment. Most research done in the field of Q-learning has focused on discrete domains, although the environment with which the agent must interact is generally continuous. Thus we need to devise some methods that enable Q-learning to be applicable to the continuous problem domain. In this paper, an extended fuzzy rule is proposed so that it can incorporate Q-learning. The interpolation technique, which is widely used in memory-based learning, is adopted to represent the appropriate Q value for current state and action pair in each extended fuzzy rule. The resulting structure based on the fuzzy inference system has the capability of solving the continuous state about the environment. The effectiveness of the proposed structure is shown through simulation on the cart-pole system.

• Journal of Biosystems Engineering
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• 제23권4호
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• pp.365-372
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• 1998

This study was carried out to develop a nutrient-solution mixing-and-supplying system, which used a low-cost metering device instead of expensive metering pumps and a fuzzy logic controller. A low cost and precise overflow-type metering device was developed and evaluated by testing the flow discharge for the automatic nutrient-solution mixing-and-supplying system for snail-scale hydroponic sewers. The fuzzy logic controllers, which could predict and meet the desired values of EC and supply rate of nutrient solution were developed and verified by simulation and experiment. this fuzzy logic controller, whose algorithm consists of four crisp inputs, two crisp outputs and nine rules, was developed to predict the desired value of EC and supply rate of nutrient solution and two crisp inputs, one crisp output and nine rules used to control EC to the desired values. The nutrient-solution mixing-and-supplying system showed satisfactory EC control performance with the maximum overshooting of 0.035 mS/cm and the maximum settling time of 15 minutes in case of increasing 0.7 mS/cm. also, the accuracy of the overflow-type metering device in terms of the full-scale error was 2.29% when using solenoid valve only and 0.2% when using solenoid valve and flow control valve together.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.937-940
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• 1993

A new hardware architecture achieves high speed, high precision fuzzy inference capabilities while maintaining Flexibility on par with software approaches. This flexibility allows unmodified, uncompromised porting of fuzzy system designs into hardware. The architecture is also scalable and offers data resolutions from 8 bits to 32 bits.

• Journal of Power Electronics
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• 제17권6호
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• pp.1587-1599
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• 2017

This paper proposes a real-time implementation of an optimal operation of a double stage grid connected wind power system incorporating an active power filter (APF). The system is used to supply the nonlinear loads with harmonics and reactive power compensation. On the generator side, a new adaptive neuro fuzzy inference system (ANFIS) based maximum power point tracking (MPPT) control is proposed to track the maximum wind power point regardless of wind speed fluctuations. Whereas on the grid side, a modified predictive current control (PCC) algorithm is used to control the APF, and allow to ensure both compensating harmonic currents and injecting the generated power into the grid. Also a type 2 fuzzy logic controller is used to control the DC-link capacitor in order to improve the dynamic response of the APF, and to ensure a well-smoothed DC-Link capacitor voltage. The gained benefits from these proposed control algorithms are the main contribution in this work. The proposed control scheme is implemented on a small-scale wind energy conversion system (WECS) controlled by a dSPACE 1104 card. Experimental results show that the proposed T2FLC maintains the DC-Link capacitor voltage within the limit for injecting the power into the grid. In addition, the PCC of the APF guarantees a flexible settlement of real power exchanges from the WECS to the grid with a high power factor operation.

• 한국주거학회논문집
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• 제22권3호
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• pp.113-122
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• 2011

This study aimed to develop AI- (Artificial Intelligence) based thermal control logics and test their performance for identifying the optimal thermal control method in buildings. For this objective, a conventional Two-Position On/Off logic and two AI-based variable logics, which applied ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System), have developed. Performance of each logic was tested in a typical two-story residential building in U.S.A. using the computer simulation incorporating MATLAB and IBPT (International Building Physics Toolbox). In the analysis of the test results, AI-based control logic presented the advanced thermal comfort with stability compared to the conventional logic while they did not show significant energy saving effects. In conclusion, the predictive and adaptive AI-based control logics have a potential to maintain interior air temperature more comfortably, and the findings in this study could be a solid foundation for identifying the optimal thermal control method in buildings.

• 한국콘텐츠학회논문지
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• 제9권6호
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• pp.45-52
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• 2009

최근 제조업에서 제품 설계를 지원하는 디지털 전문가 시스템을 개발하는 사례가 일어나고 있다. 이 시스템은 제조업에서 엔지니어가 프로세스를 통제하고, 생산관리와 시스템 관리 등을 위하여 사용되고 있다. 본 논문에서는 전문가 시스템을 구축하기 위한 온톨로지 기반의 추론 엔진 개발에 대하여 논한다. 전문가 시스템은 한국어를 지원하고 다양한 기능을 가지며, 그래픽한 온톨로지 맵 인터페이스와 퍼지 룰 기능 정의 등의 기능을 갖도록 하였다. 또한, 온톨로지 맵 구축과 온톨로지 기반의 퍼지 추론 방법에 대하여 지식을 표현하는 방법에 대하여 설명한다.

• 제어로봇시스템학회논문지
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• 제7권6호
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• pp.494-501
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• 2001

In this paper an ANFIS(Adativo Neuro-Fuzzy Inference System)- based fault detection and diagnosis for a closed loop control system is proposed. The proposed diagnostic system contains two ANFIS. One is run as a parallel model within the model in closed loop control(MCL) and the other is run as a series-parallel model within the process in closed loop(PCL) for the generation of relevant symptoms for fault diagnosis. These symptoms are further processed by another classification logic with simple rules and neural network for process and controller fault diagnosis. Experimental results for a DC shunt motor control system illustrate the effectiveness of the proposed diagnostic scheme.

• 대한교통학회지
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• 제21권3호
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• pp.71-83
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• 2003

교차로의 신호시간 계획이나 간선도로 축의 제어에 있어서 가장 대표적인 문제는 수시로 변화하는 교통상황이다. 또한 이러한 변화로 인해 정확한 교통 데이터를 얻기 힘들고, 그에 대한 분석 또한 어렵다. 따라서 본 논문에서는 이러한 불명확한 교통데이터를 이용하여 교차로 및 간선도로의 제어를 하기 위해, 인간의 사고와 유사한 추론이 가능하다고 판단되는 Fuzzy Logic을 적용함으로써 불명확한 상황에 대하여 수학적인 함수로 표현되지는 않지만 언어적인(Linguistic) 제어가 가능하도록 하여, 기존의 교통제어 방법보다 교통상황에 민감하게 대처할 수 있는 새로운 제어전략을 제시하였다. 본 연구는 "영상검지기를 이용한 실시간 교통신호 감응제어(김성호, 1996)"의 독립교차로의 신호 제어 부분을 기초로 하여 간선도로 상의 연속진행 제어에 대한 전략을 제안하고, 그 효과를 기존의 제어 방법에 의한 효과와 비교·분석하였다. 또한 각 제어 방법에 대한 분석을 위하여, 교통 시뮬레이션 소프트웨어인 TRAF-NETSIM을 이용하여 각각의 효과를 비교하였다.