• 한국멀티미디어학회논문지
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• 제18권3호
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• pp.387-400
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• 2015

A fuzzy logic based mobile robot navigation system was developed to improve the driving ability without trapping inside obstacles in complex terrains, which is one of the most concerns in robot navigation in unknown terrains. The navigation system utilizes the data from ultrasonic sensors to recognize the distances from obstacles and the position information from a GPS sensor. The fuzzy navigation system has two groups of behavior rules, and the robot chooses one of them based on the information from sensors while navigating for the targets. In plain terrains the robot with the proposed algorithm uses one rule group consisting of behavior rules for avoiding obstacle, target steering, and following edge of obstacle. Once trap is detected the robot uses the other rule group consisting of behavior rules strengthened for following edge of obstacle. The output signals from navigation system control the speed of two wheels of the robot through the fuzzy logic data process. The test was conducted in the Matlab based mobile robot simulator developed in this study, and the results show that escaping ability from obstacle is improved.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제3권2호
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• pp.138-145
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• 2003

Performance assessment was introduced to improvement of self-directed learning and method of assessment for differenced learning as the seventh educational curriculum is enforced. Performance assessment is overcoming limitation about problem solving ability and higher thinking abilities assessment that is problem of a written examination and get into the spotlight by way for quality of class and school normalization. But, performance assessment has problems about possibilities of assessment fault by appraisal, fairness, reliability, and validity of grading, ambiguity of grading standard, difficulty about objectivity security etc. This study proposes fuzzy performance assessment system to solve problem of the conventional performance assessment. This paper presented an objective and reliable performance assessment method through fuzzy reasoning, design fuzzy membership function and define fuzzy rule analyzing factor that influence in each sacred ground of performance assessment to account principle subject. Also, performance assessment item divides by formation estimation and subject estimation and designed membership function in proposed performance assessment method. Performance assessment result that is worked through fuzzy performance assessment system can pare down burden about appraisal's fault and provide fair and reliable assessment result through grading that have correct standard and consistency to students.

• 한국시뮬레이션학회논문지
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• 제9권1호
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• pp.21-38
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• 2000

A fuzzy dispatching algorithm with adaptable control scheme is proposed for more flexible and adaptable operation of AGV system. The basic idea of the algorithm is prioritization of all move requests based on the fuzzy urgency. The fuzzy urgency is measured by the fuzzy multi-criteria decision-making method, utilizing the relevant information such as incoming and outgoing buffer status, elapsed time of move request, and AGV traveling distance. At every dispatching decision point, the algorithm prioritizes all move requests based on the fuzzy urgency. The performance of the proposed algorithm is compared with several dispatching algorithms in terms of system throughput in a hypothetical job shop environment. Simulation experiments are carried out varying the level of criticality ratio of AGVs , the numbers of AGVs, and the buffer capacities. The rule presented in this study appears to be more effective for dispatching AGVs than the other rules.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.275-278
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• 1996

The application of genetic algorithms to fuzzy rule generation holds a great deal of promise in overcoming difficult problems in fuzzy systems design. There are some aspects to be considered when genetic algorithms are used for generating fuzzy rules. In this paper, we will present an aspect about the control surface constructed by the resultant rules. In the extensive simulations, an important observation that the rules searched by genetic algorithms are randomly scattered is made and a solution to this problem is provided by including a smoothness cost in the objective function. We apply the fuzzy rules generated by genetic algorithms to the fuzzy truck backer-upper control system and compare them with the rules made by an expert.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.675-677
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• 2005

This study proposes a fuzzy classification using EM algorithm. For cluster validation, this approach iteratively estimates the class-parameters in the fuzzy training for the sample classes and continuously computes the log-likelihood ratio of two consecutive class-numbers. The maximum ratio rule is applied to determine the optimal number of classes.

• 한국컴퓨터정보학회논문지
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• 제4권4호
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• pp.1-8
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• 1999

인간의 사고 방법과 그 원리를 연구하는 논리에서 이진 논리는 True 혹은 False 중 하나만을 진리값으로 갖는 명제를 다룬다. 인간이 다루는 대부분이 기존의 이치논리는 다루기 힘든 애매성(Fuzziness)을 포함하고 있음에도 불구하고 이를 표현하는 지식은 부정확하며 신뢰성은 떨어지게 된다. 이러한 문제점을 해결하기 위하여 본 논문에서는 다치 논리를 사용하였으며, 다치 논리는 2치 논리에 비하여 동일정보량을 처리하는데 고속 처리가 가능한 장점을 가지고 있다. 그래서 2치 부호화를 기본으로한 정보시스템에 비해 다치 부호화 알고리즘을 사용하여 구성하는 기호다치논리를 사용하여 Fuzzy Inference에서 사용되는 Fuzzy Rule을 효과적으로 Minimization하여 추론이 가능하도록 하는 것이다.

• Journal of Advanced Marine Engineering and Technology
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• 제27권1호
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• pp.42-48
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• 2003

In general many engineers can diagnose the fault condition using the abnormal ones among data monitored from a diesel engine, but they don't need the system modelling or identification for the work. They check the abnormal data and the relationship and then catch the fault condition of the engine. This paper proposes the construction of a fault diagnosis engine through malfunction data gained from the data fault detection system of neural networks for diesel generator engine, and the rule inference method to induce the rule for fuzzy inference from the malfunction data of diesel engine like a site engineer with a fuzzy system. The proposed fault diagnosis system is constructed in the sense of the Malfunction Diagnosis Engine(MDE) and Hierarchy of Malfunction Hypotheses(HMH). The system is concerned with the rule reduction method of knowledge base for related data among the various interactive data.

• International Journal of Control, Automation, and Systems
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• 제3권2호
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• pp.183-194
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• 2005

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.24.2-27
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• 1996

This paper is a study on the fuzzy force control of a miniature gripper driven by piezoelectric bimorph actuator. The system is composed of two flexible cantilevers, a stepping motor, a laser displacement transducer and two semiconductor force sensors attached to the beams. Obtained results show that the present artificial finger system works well as a miniature gripper, which produces approximately 0.06N force in the maximum. Further, the fuzzy position/force control algorithm is applied to the soft-handing gripper for stable grasping of a object. It revealed that the fuzzy rule-based controller be efficient controller for the stable drive of the flexible miniature gripper. It also showed that two semiconductor strain gauges located in the flexible beam play an important roles for force control, position control and vibration suppression control.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.50-54
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• 1997

In general, the design of fuzzy logic controller has difficulties in the acquisition of expert's knowledge. So, some methods that can optimize the parameters for fuzzy logic controller automatically without expert knowledge was provided. Recently, tuning method for fuzzy logic controller using genetic algorithm(GA) were proposed in many papers. However, those are tuning methods for a part or some part of fuzzy logic controller. In this paper, we proposes auto tuning method for the whole part of tuzzy logic controller, such as parameters of membership functions for antecedence and consequence parts, rule base, scaling factor and the number of rule. Finally, second order dead time plant is provided to show the advantages of the proposed method.