• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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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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• v.3 no.3
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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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• v.23 no.4
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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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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• v.17 no.6
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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.

• Journal of the Korean housing association
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• v.22 no.3
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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.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.45-52
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• 2009

Recently, we started a project development of the digital expert system for the product design supporting in manufacturing industry. This digital expert system is used to the engineers in manufacturing industry for the process control, production management and system management. In this paper, we develop the ontology based inference engine shell for building of expert system. This expert system shell included a various functions which of Korean language supporting, graphical ontology map modeling interface, fuzzy rule definition function and etc. And, we introduce the knowledge representation method for the ontology map building and ontology based fuzzy inferencing method.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.6
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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.

• Journal of Korean Society of Transportation
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• v.21 no.3
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• pp.71-83
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• 2003

An arterial street control is performed for the purpose of the progression of a traffic flow using the arterial. However during the progression in the arterial, the change according to the time is one of the most representative problems occurring at a signal plan. This paper intends to efficiently operate the arterial progression by applying fuzzy logic, which is thought to be the most possible one in the inference as that of the human logic, to the traffic responsive control system. Fuzzy Logic controller is appliable to the daily human language (linguistic). can be dealt with the uncertain traffic data and is useful on planning the signal control to sensitively confront the randomly changing traffic condition. This study, based on the signal control part of the isolated intersection in "A Development of a Real-time, Traffic Adaptive Control Scheme Through VIDs"(Seong Ho. Kim. 1996). suggested the strategy for the progression control in the arterial and analyzed its effect by comparing the effect of the existing control method. In addition, the study compared each effect by using TRAF-NETSIM which is the traffic simulation software to analyze each control method.