• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.1
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• pp.23-34
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• 2002

This paper proposes a new neuro-fuzzy system that is fast trained by evolution strategy and describes application results of the proposed system to nonlinear system identification to show its usefulness. As training methods of neuro-fuzzy systems, modified error back-propagation algorithms and genetic algorithms have been used so far. However, the former has some drawbacks such as long training time, falling to local optimum, and experimental selecting of learning rates and the latter has difficulty in precise searching solutions because genetic algorithms represents solutions as genotype individuals. The evolution strategy we used can do precise search because its individuals are represented as phenotype real values, it seldom falls into a local optimum, and its training speed is faster than error back-propagation algorithms. We apply our neuro-fuzzy systems to nonlinear system identification. It was found from experiments that training speed is fast and the training results were considerably good.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.12
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• pp.1635-1642
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• 2012

The identification of the dynamic properties of structural joints is important for predicting the dynamic behavior of assembled systems. However, the identification of the properties using analytical or experimental approaches is extremely difficult or even impossible. Several studies have proposed hybrid or synthesis methods that simultaneously used analytical and experimental approaches to identify the dynamic properties of a joint. However, among the many types of joints, only the bolt joint was treated as a practical example in these studies. In this study, for a simple assembly system comprising two plates and one hinge joint, a simple methodology involving the use of the static-based subpart analysis method to identify the dynamic properties is proposed. Finally, the proposed method is applied to a glove box in a passenger vehicle that includes hinge joints.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.74-82
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• 2011

To do fuzzy modelling of a nonlinear process needs to analyze the characteristics of input-output of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods. For this, fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the fuzzy rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the clusters are used for identification of fuzzy model and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. In the consequence part of the fuzzy rules fuzzy reasoning is conducted by two types of inferences such as simplified and linear inference. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. And lastly, using gas furnace process which is widely used in nonlinear process we evaluate the performance and the system characteristics.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.9
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• pp.1040-1049
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• 1992

When the model of control object is not described correctly, ambiguity is often expressed by unknown parameter, In a case that this ambiguity satisfies a certain condition of limit, if robust control method is used, even if model is not correctly discribed, control system can be composed. The characteristic of control based on the variable structure theory is that the influence by ambiguity of system eradicates high-gain feedback. Therefore in this paper, VSS indentifier is proposed. Transformation of control input producing control system in sliding mode actually reflects influence of ambiguity unknown parameter of control object. If useful information is out from transformation input by a few times of operation, proper identify mechanism is selected and this information is used, to decide the unknown parameter is possible. So more effective controller was composed by addition of the proposed identifier to the unknown parameter identifier of robot manipulator.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.429-432
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• 2004

본 논문은 기존의 퍼지 다항식 뉴럴 네트워크 (Fuzzy Polynomial Neural Networks ; FPNN) 모델을 이용하여 비선형성 데이터에 대한 추론을 제안한다. 복잡한 비선형 시스템의 모델동정을 위하여 생성된 GMDH 방법에 기초한 FPNN의 각 노드는 퍼지 규칙을 기반으로 구축되었으며, 층이 진행되는 동안 모델 스스로 노드의 선택과 제거를 통해 최적의 네트워크 구조를 생성할 수 있는 유연성을 가지고 있다. FPNN 각각의 활성노드를 퍼지다항식 뉴론(Fuzzy Polynomial Neuron ; FPN)이라고 표현한다. FPNN의 후반부 구조는 입출력 변수 사이 의 간략과 회귀다항식 (1차, 2차, 변형된 2차식) 함수에 의해 구현된다. 규칙의 전반부 멤버쉽 함수는 삼각형과 가우시안형의 멤버쉽 함수가 사용된다. 또한 유전자 알고리즘을 사용하여 각노드의 부분표현식을 구성하는 입력변수의 수, 입력변수와 차수의 선택 동조를 통하여 최적의 Genetic Algorithms(GAs)을 이용한 FPNN모델을 설계하는 것이 유용하고 효과적임을 보인다.

• Science of Emotion and Sensibility
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• v.1 no.2
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• pp.93-103
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• 1998

효율적이고 체계적인 퍼지제어를 위해 조작자의 제어동작을 모델링하거나 공정을 모델링하는 기법이 필요하고, 또한 퍼지 추론시에 조건부의 기여도(contribution factor)의 결정과 동작부의 제어량의 결정이 추론의 결과에 중요하다. 본 논문에서는 추론시 조건부의 기여도와 동작부의 세어량이 퍼지 엔트로피의 개념하에서 수행되는 적응 퍼지 추론시스템을 제시한다. 제시된 시스템은 전방향 신경회로망의 토대위에서 구현되며 주건부의 기여도가 퍼지 엔트로피에 의하여 구해지고, 동작부의 제어량은 확장된 퍼지 엔트로피에 의하여 구해진다. 이를 위한 학습 알고리즘으로는 역전파 알고리즘을 이용하여 조건부의 파라미터의 동정을 하고 동작부 파라미터의 동정에는 국부해에 보다 강인한 유전자 알고리즘을 이용하다. 이러한 모델링 기법을 임펄스 잡음과 가우시안 잡음이 첨가된 영상에 적용하여 본 결과, 영상복원시에 발생되는 여러 가지의 경우에 대한 적응성이 보다 양호하게 유지되었고, 전체영상의 20%의 데이터만으로도 객관적 화질에 있어서 기존의 추론 방법에 비해 향상을 보였다.

• NEWSLETTER 전기공업
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• no.93-19 s.92
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• pp.38-40
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• 1993

• STIMA Bulletin
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• s.3
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• pp.6-11
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• 2005

• NEWSLETTER 전기공업
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• no.93-25 s.97
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• pp.39-42
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• 1993

• NEWSLETTER 전기공업
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• no.97-14 s.183
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• pp.35-37
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• 1997