• Journal of the military operations research society of Korea
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• v.26 no.2
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• pp.55-74
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• 2000

A knowledge-based with production rules is a representation of static knowledge of an expert. On the other hand, a real system such as the stock market is dynamic in nature. Therefore we need a method to reflect the dynamic nature of a system when we make inferences with a knowledge-based system. This paper suggests a strategy of dynamic inference that can be used to take into account the dynamic behavior of decision-making with the knowledge-based system consisted of fuzzy production rules. A degree of match(DM) between actual input information and a condition of a rule is represented by a value [0,1]. Weights of relative importance of attributes in a rule are obtained by the AHP(Analytic Hierarchy Process) method. Then these weights are applied as exponents for the DM, and the DMs in a rule are combined, with the Min operator, into a single DM for the rule. In this way, the importance of attributes of a rule, which can be changed from time to time, can be reflected in an inference with fuzzy production systems.

• Journal of the Korean Society of Safety
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• v.36 no.2
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• pp.49-57
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• 2021

In the case of military supplies, any potential failure and causes of failures must be considered. This study is aimed at examining the failure modes of a rotorcraft landing system to identify the priority items. Failure mode and effects analysis (FMEA) is applied to the rotorcraft landing system. In general, the FMEA is used to evaluate the reliability in engineering fields. Three elements, specifically, the severity, occurrence, and detectability are used to evaluate the failure modes. The risk priority number (RPN) can be obtained by multiplying the scores or the risk levels pertaining to severity, occurrence, and detectability. In this study, different weights of the three elements are considered for the RPN assessment to implement the FMEA. Furthermore, the FMEA is implemented using a fuzzy rule base, similarity aggregation model (SAM), and grey theory model (GTM) to perform a comparative analysis. The same input data are used for all models to enable a fair comparison. The FMEA is applied to military supplies by considering methodological issues. In general, the fuzzy theory is based on a hypothesis regarding the likelihood of the conversion of the crisp value to the fuzzy input. Fuzzy FMEA is the basic method to obtain the fuzzy RPN. The three elements of the FMEA are used as five linguistic terms. The membership functions as triangular fuzzy sets are the simplest models defined by the three elements. In addition, a fuzzy set is described using a membership function mapping the elements to the intervals 0 and 1. The fuzzy rule base is designed to identify the failure modes according to the expert knowledge. The IF-THEN criterion of the fuzzy rule base is formulated to convert a fuzzy input into a fuzzy output. The total number of rules is 125 in the fuzzy rule base. The SAM expresses the judgment corresponding to the individual experiences of the experts performing FMEA as weights. Implementing the SAM is of significance when operating fuzzy sets regarding the expert opinion and can confirm the concurrence of expert opinion. The GTM can perform defuzzification to obtain a crisp value from a fuzzy membership function and determine the priorities by considering the degree of relation and the form of a matrix and weights for the severity, occurrence, and detectability. The proposed models prioritize the failure modes of the rotorcraft landing system. The conventional FMEA and fuzzy rule base can set the same priorities. SAM and GTM can set different priorities with objectivity through weight setting.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.2
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• pp.48-54
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• 2002

In this study, a neuro-fuzzy controller which has the characteristic of fuzzy control and artificial neural network is designed. A fuzzy rule to be applied is automatically selected by the allocated neurons. The neurons correspond to fuzzy rules are created by an expert. To adapt the more precise model is implemented by error back-propagation learning algorithm to adjust the link-weight of fuzzy membership function in the neuro-fuzzy controller. The more classified fuzzy rule is used to include the property of dual mode method. In order to verify the effectiveness of the proposed algorithm designed above, an operating characteristic of a DC servo motor with variable load is investigated.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.231-240
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• 2001

Conventional expert systems has been criticized due to its lack of capability to adapt to the changing decision-making environments. In literature, many methods have been proposed to make expert systems more environment-adaptive by incorporating fuzzy logic and neural networks. The objective of this paper is to propose a new approach to building a self-evolving expert system inference mechanism by integrating fuzzy neural network and fuzzy rule extraction technique. The main recipe of our proposed approach is to fuzzify the training data, train them by a fuzzy neural network, extract a set of fuzzy rules from the trained network, organize a knowledge base, and refine the fuzzy rules by applying a pruning algorithm when the decision-making environments are detected to be changed significantly. To prove the validity, we tested our proposed self-evolving expert systems inference mechanism by using the bankruptcy data, and compared its results with the conventional neural network. Non-parametric statistical analysis of the experimental results showed that our proposed approach is valid significantly.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.93-99
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• 2009

The fuzzy control, neural network and genetic algorithm(GA) are algorithms to make the intelligence of system more higher. In this paper, we optimized the fuzzy controller using a genetic algorithm for desire response. Also a compensated fuzzy controller has dual rules. One control rule used to decrease the overshoot and rise time occurring in transient response region and another fuzzy control rule use to decrease the steady state error and rapildy to converge at the convergence region. GA is necessary to optimal the exchange time of the two fuzzy control rule base. Fuzzy-GA controller have a process of reproduction, crossover and mutation and we experimented by hydraulic servo motor control system We could observe that compensated Fuzzy-GA controller have good control performance compare to the fuzzy control technique have two rule base table.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.253-257
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• 2000

the objective of this study is to design and evaluate a methodology for diagnosing the appendicitis in a fuzzy neural network that integrates the partition of input space by fuzzy entropy and the generation of fuzzy control rules and learning algorithm. In particular the diagnosis of appendicitis depends on the rule of thumb of the experts such that it associates with the region, the characteristics, the degree of the ache and the potential symptoms. In this scheme the basic idea is to realize the fuzzy rle base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by back propagation learning rule. To eliminate the number of the parameters of the rules, the output of the consequences of the control rules is expressed by the network's connection weights. As a result we obtain a method for reducing the system's complexities. Through computer simulations the effectiveness of the proposed strategy is verified for the diagnosis of appendicitis.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.05a
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• pp.194-197
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• 2004

In the tube hydroforming process, a tube is placed into the die cavity and the ends of the tube are sealed by fixing the axial cylinder piston into the ends. Then the tube is pressurized with a hydraulic fluid and simultaneously the axial cylinders move to feed the material into the expansion zone. Therefore, the quantitative relationship between process parameters such as internal pressure and feeding amount and hydroformabillity, is hard to establish because of their high complexity and many unknown factors. In this study, the empirical and the quantitative relationship between process parameters and hydroformabillity are analyzed by fuzzy rules. Fuzzy expert system is an advanced expert system which uses fuzzy rule and approximate reasoning. Many process parameters are converted to the quantitative relationship by use of approximate reasoning of fuzzy expert system. The comparison between experimentally measured hydroformabillity from hydroforming experiments and the predicted values by fuzzy expert system shows a good agreement.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2018-2020
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• 2001

In this study, a Neuro-Fuzzy Controller which has the characteristic of Fuzzy control and Artificial Neural Network is designed. A fuzzy rule to be applied is automatically selected by the allocated neurons. The neurons correspond to Fuzzy rules are created by an expert. To adapt the more precise modeling is implemented by error back propagation learning of adjusting the link-weight of fuzzy membership function in the Neuro-Fuzzy controller. The more classified fuzzy rule is used to include the property of dual mode method. In order to verify the effectiveness of an algorithm designed above, an operating characteristic of a AC servo motor is investigated.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.05
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• pp.155-160
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• 1992

Liver function test is one of the most common tests for diagnosis and follow-up of patients and for heal th screening. Automatic interpretation and suggestions on the diagnostic possibilities contribute to shorten the interpretation time of the test results and help to provide qualified health care. Fuzzy logic has been recently introduced and being spread for these purposes. The present study aims at model Ins the foray rule-based laboratory diagnosis system. The fuzzy rule-based laboratory diagnosis system was applied to the diagnosis regarding liver function test. The system was evaluated by comparing with the stepwise multivariate discriminant function analysis, which showed similar results, and the overall accuracy of the fuzzy diagnosis system was about 80%.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.121-129
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• 2006

Rule-based Expert system using Fuzzy technique inferences various rules by user's input condition and the most proper control signal. This system can lose objectivity by input condition which depends on user's decision. This paper can solve those problems by adding case-based system's technique. The traffic signal control expert system is proposed to store the cases based on the statistics, days, seasons and various circumstances and use them.