• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.358-364
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• 2007

Intelligent walking modeling of humanoid robot using learning based neuro-fuzzy system is presented in this paper. Walking pattern, trajectory of the zero moment point (ZMP) in a humanoid robot is used as an important criterion for the balance of the walking robots but its complex dynamics makes robot control difficult. In addition, it is difficult to generate stable and natural walking motion for a robot. To handle these difficulties and explain empirical laws of the humanoid robot, we are modeling practical humanoid robot using neuro-fuzzy system based on the two types of natural motions which are walking trajectories on a t1at floor and on an ascent. Learning based neuro-fuzzy system employed has good learning capability and computational performance. The results from neuro-fuzzy system are compared with previous approach.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.474-480
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• 2007

This paper presents an application of intelligent modeling method to manual control system with human operator. Human operator as a part of controller is difficult to be modeled because of changes in individual characteristics and operation environment. So in these situation, a fuzzy model developed relying on the expert's experiences or trial and error may not be acceptable. To supplement the fuzzy model block, a neural network based modeling error compensator is incorporated. The feasibility of the present fuzzy-neural modeling scheme has been investigated for the real human based target tracking system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.7
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• pp.30-41
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• 1992

As a general model of rule-based systems, we propose a model for a fuzzy production system having chaining rules and an inference engine associated with the model. The concept of so-called 'fuzzy petri nets' is used to model the fuzzy production system and the inference engine is designed to be capable of handling inexact knowledge. The fuzzy logic is adopted to represent vagueness in the rules and the certainty factor is used to express uncertainty of each rules given by a human expert. Parallel, inference schemes are devised by transforming Fuzzy Petri nets to matrix formula. Futher, the inference engine mechanism under the Mamdani's implication method can be desceribed by a simple algebraic formula, which makes real time inference possible.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.137-142
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• 2006

상황은 단일 사건에 의해 결정되는 경우도 있지만, 많은 경우 일련의 사건이 특정 시간 제약을 만족하면서 발생할 때 상황이 결정된다. 따라서 상황에 대한 추론은 시간 제약 조건 만족 여부와 함께 사건의 발생을 순서를 확인하는 방법으로 수행될 수 있다. 한편, 어떤 상황은 분명하게 정의되는 것이 아니라 애매한 개념을 사용하여 기술되기 때문에, 퍼지 개념을 이용한 상황 기술과 이에 대한 추론이 필요하다. 한편, 유비쿼터스 환경에서와 같이 여러 대상에 대한 상황을 유추하여 서비스를 제공해야 하는 경우에, 대상 간에 동일한 상황이 발생할 수 있기 때문에 이에 대한 고려가 필요하다. 이러한 상황 추론을 위해서 이 논문에서는 Fuzzy Colored Timed Petri net 모델이라는 상황 추론 모델에 대해서 제안한다. 제안한 모델은 Timed Petri net 성질을 이용하여 일련의 사건 발생을 모델링하고, Colored Petri net의 성질을 이용하여 다수 대상에 대한 상황 추론을 허용하며, fuzzy 토큰 개념을 이용하여 애매한 개념을 사용하여 정의된 상황에 대한 추론을 가능하게 한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.273-276
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• 2004

Genome-scale expression data provides us with valuable insights about organisms, but the biological validation of in-silico analysis is difficult and often controversial. Here we present a new approach for integrating previously established knowledge with computational analysis. Based on the known biological evidences, IGAM (Integrated Gene Association Matrix) automatically estimates the relatedness between a pair of genes. We combined this association knowledge to the regulatory network modeling and fuzzy clustering in yeast 5. Cerevisiae. The result was found to be more effective for extracting biological meanings from in-silico inferences for gene expression data.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.522-524
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• 1999

This paper addresses the systematic approach to the fuzzy modeling of the class of complex and uncertain nonlinear systems. While the conventional genetic algorithm (GA) only searches the global solution, Virus-Evolutionary Genetic Algorithm(VEGA) can search the global and local optimal solution simultaneously. In the proposed method the parameter and the structure of the fuzzy model are automatically identified at the same time by using VEGA. To show the effectiveness and the feasibility of the proposed method, a numerical example is provided. The performance of the proposed method is compared with that of conventional GA.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.493-495
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• 2005

This paper proposes a swarm-based fuzzy system modeling technique for coke oven combustion control diagnosis. The coke plant produces coke for the blast furnace plant in steel making process by charging coal into oven and supplying gas to carbonize it. A conventional mathematical model for coke oven combustion control has been used to control the amount of gas input, but it does not work well because of highly nonlinear feature of coke plant. To solve this problem, swarm-based fuzzy system modeling technique is suggested to construct a diagnosis model of coke oven combustion control. Based on the measured input-output data pairs, the fuzzy rules are generated and the parameters are tuned by the PSO(Particle Swarm Optimizer) to increase the accuracy of the fuzzy system is operated. This system computes the proper amount of gas input taking the operation conditions of coke oven into account, and compares the computed result with the supplied gas input.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.502-506
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• 1992

In this study, we proposed the fuzzy modeling method and designed a model-based logic controller for Activated and Sludge Process(A.S.P.) in sewage treatment. The identification of the structure of fuzzy implications is carreid out by use of fuzzy c-means clustering algorithm. And to identify the parameters of fuzzy implications, we used the complex and the least square method. To tune the premise parameters automatically the complex method is implemented. The model-based fuzzy controller is designed by rules generated from the identified A.S.P. fuzzy model. The feasibility of the proposed approach is evaluated through the identification of the fuzzy model to describe an input-output relation of the A.S.P.. The performance of identified model-based fuzzy controller is evaluated through the computer simulations.

• Proceedings of the KSR Conference
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• 2006.11a
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• pp.145-159
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• 2006

This paper describes the development of a clustering based fuzzy controller of an electromagnetic suspension vehicle using gain scheduling method and Kalman filter for a simplified single magnet system. Electromagnetic suspension vehicle systems are highly nonlinear and essentially unstable systems For achieving the levitation control of the DC electromagnetic suspension system, we considered a fuzzy system modeling method based on clustering algorithm which a set of input/output data is collected from the well defined Linear Quadratic Gaussian(LQG) controller. Simulation results show that the proposed clustering based fuzzy controller methodology robustly yields uniform performance with adequate gap response over the mass variation range.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.1
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• pp.6-12
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• 2011

In this study, we proposed a residential load modeling method based on neuro-fuzzy inference system by considering of various harmonics. The developed method was implemented by using harmonic information, fundamental frequency and voltage which are essential input factors in conventional method. Thus, the proposed method makes it possible to effectively estimate load characteristics in power lines with harmonics. To show the effectiveness, the proposed method has been intensively tested with various dataset acquired under the different frequency and voltage and compared it with a conventional method based on neural networks.