• Title/Summary/Keyword: fuzzy approximate reasoning

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Determination and Optimization of welding condition using Fuzzy Expert System for MAG-Welding (퍼지 전문가 시스템을 활용한 적정 용접조건의 설정과 최적화)

  • J.Y. Park
    • Journal of the Society of Naval Architects of Korea
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    • v.32 no.4
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    • pp.136-141
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    • 1995
  • Determination and optimization of proper welding condition are very important tasks to be directly related to weld quality and productivity. On this research the relationship between welding parameters and results is investigated systematically. Theoretical method, statistical analysis of experimental data and analysis of empirical knowledge are applied for this work. These results are represented by empirical equations, fuzzy rules and artificial intelligent knowledge forms in the knowledge base. The approximate reasoning of fuzzy expert system and the information in the knowledge base are used for recommendation of suitable welding condition, and optimization of welding parameter which is based on the evaluation of welding results by user.

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Development of Fuzzy Inference Engine for Servo Control Using $\alpha$-level Set Decomposition ($\alpha$ -레벨집합 분해에 의한 서보제어용 퍼지 추론 연산회로의 개발)

  • 홍순일;이요섭
    • Journal of the Institute of Convergence Signal Processing
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    • v.2 no.3
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    • pp.50-56
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    • 2001
  • As the fuzzy control is applied to servo system, the hardware implementation of the fuzzy information systems requires the high speed operations, short real time control and the small size systems. The aims of this study is to develop hardware of the fuzzy information systems to be apply to servo system. In this paper, we propose a calculation method of approximate reasoning for fuzzy control based on $\alpha$ -level set decomposition of fuzzy sets by quantize $\alpha$ -cuts. This method can be easily implemented with analog hardware. The influence of quantization Bevels of $\alpha$-cuts on output from fuzzy inference engine is investigated. It is concluded that 4 quantization levels give sufficient result for fuzzy control performance of dc servo system. The hardware implementation of proposed operation method and of the defuzzification by gravity center method which is directly converted to PWM actuating signal is also presented. It is verified useful with experiment for dc servo system.

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On Developing The Intellingent contro System of a Robot Manupulator by Fussion of Fuzzy Logic and Neural Network (퍼지논리와 신경망 융합에 의한 로보트매니퓰레이터의 지능형제어 시스템 개발)

  • 김용호;전홍태
    • Journal of the Korean Institute of Intelligent Systems
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    • v.5 no.1
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    • pp.52-64
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    • 1995
  • Robot manipulator is a highly nonlinear-time varying system. Therefore, a lot of control theory has been applied to the system. Robot manipulator has two types of control; one is path planning, another is path tracking. In this paper, we select the path tracking, and for this purpose, propose the intelligent control¬ler which is combined with fuzzy logic and neural network. The fuzzy logic provides an inference morphorlogy that enables approximate human reasoning to apply to knowledge-based systems, and also provides a mathematical strength to capture the uncertainties associated with human cognitive processes like thinking and reasoning. Based on this fuzzy logic, the fuzzy logic controller(FLC) provides a means of converhng a linguistic control strategy based on expert knowledge into automahc control strategy. But the construction of rule-base for a nonlinear hme-varying system such as robot, becomes much more com¬plicated because of model uncertainty and parameter variations. To cope with these problems, a auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), that is known to be very effective in the optimization problem, will be proposed. The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.

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Implementation of an interval Based expert system for diagnoisis of Oriental Traditional Medicine

  • Phuong, Nguyen-Hoang;Duong, Uong-Huong;Kwak, Yun-Sik
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2001.01a
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    • pp.486-495
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    • 2001
  • This paper describes an implementation of the interval based expert system for syndrome differential diagnosis of Oriental Traditional Medicine (OTM). An approximate reasoning model using fuzzy logic for syndrome differential diagnosis is proposed. Based on this model, we implemented the system for diagnosing Eight rule diagnosis, organ diagnosis and then final differential syndrome of OTM. After carrying out inference process, the system will provide patient\`s syndromes differentiation diagnosis in the intervals and will give the explanation, which helps the user to understand the obtained conclusions.

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On the Minimax Disparity Obtaining OWA Operator Weights

  • Hong, Dug-Hun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.19 no.2
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    • pp.273-278
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    • 2009
  • The determination of the associated weights in the theory of ordered weighted averaging (OWA) operators is one of the important issue. Recently, Wang and Parkan [Information Sciences 175 (2005) 20-29] proposed a minimax disparity approach for obtaining OWA operator weights and the approach is based on the solution of a linear program (LP) model for a given degree of orness. Recently, Liu [International Journal of Approximate Reasoning, accepted] showed that the minimum variance OWA problem of Fuller and Majlender [Fuzzy Sets and Systems 136 (2003) 203-215] and the minimax disparity OWA problem of Wang and Parkan always produce the same weight vector using the dual theory of linear programming. In this paper, we give an improved proof of the minimax disparity problem of Wang and Parkan while Liu's method is rather complicated. Our method gives the exact optimum solution of OWA operator weights for all levels of orness, $0\leq\alpha\leq1$, whose values are piecewise linear and continuous functions of $\alpha$.

A Study on Evaluating the Level of Service for Bridges using Fuzzy Approximate Reasoning (퍼지근사추론을 이용한 교량 서비스 수준 산정에 관한 연구)

  • Jo, Byung-Wan;Kim, Heon;Kim, Jang-Wook;Chi, Se-Young
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.18 no.8
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    • pp.8-17
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    • 2017
  • Infrastructures such as bridges and tunnels are crucial elements of national economic growth, and sudden collapses may lead to great catastrophes with significant social and economic losses, as well as a loss of lives. Hence, an efficient maintenance technique must be applied to guarantee safety, secure budgets to maintain a certain level of service, and prevent maintenance expenditures from being concentrated in a specific time period. Developed countries have experienced rapid increases in maintenance budgets, and maintenance costs now account for about 40% of the total maintenance budget. The level of service in asset management systems is an essential element for setting management goals and making priority decisions. Therefore, this study uses fuzzy theory to develop a new way to assess the level of service.The assessment model was applied to an actual bridge to evaluate the level of service for users.