• 제목/요약/키워드: Neuro fuzzy network

검색결과 192건 처리시간 0.038초

유압서보 시스템을 위한 뉴로-퍼지 제어기 설계 (Design of a Neuro-Euzzy Controller for Hydraulic Servo Systems)

  • 김천호;조형석
    • 대한기계학회논문집
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    • 제17권1호
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    • pp.101-111
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    • 1993
  • 본 연구에서는 제안된 뉴로-퍼지 제어기를 사용하여 유압 서보 시스템을 제어 하고 학습하기 위한 구조로써 유압 서보 시스템의 모델링을 위한 추가적인 노력이 필 요없는 feedback error learning 구조물 채택하였다. 학습 과정에서 필요한 유압 서 보 시스템의 입-출력 사이의 감도(sensitivity)의 효과는 학습 계수에 포함된다. 이 러한 형태의 제어기가 유압 서보 시스템 제어에 유용하게 적용될 수 있다는 것을 보이 기 위해서 불확실성과 높은 비선형성 뿐만아니라 외란의 영향을 받는 유압 서보 시스 템을 대상으로 시뮬레이션을 수행했다. 시뮬레이션 결과에 의하면 제안된 뉴로-퍼지 제어기는 수학적인 모델을 기초로한 기존의 제어 알고리즘에 비해 쉽게 구성할 수 있 고 높은 정밀도, 빠른 학습 속도를 얻을 수 있는 장점을 가지고 있음을 알 수 있다.

퍼지신경망을 사용한 네이브 베이지안 분류기의 분산 그래프 학습 (Learning Distribution Graphs Using a Neuro-Fuzzy Network for Naive Bayesian Classifier)

  • 전설위;임준식
    • 디지털융복합연구
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    • 제11권11호
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    • pp.409-414
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    • 2013
  • Naive Bayesian classifiers 네이브 베이지안 분류기는 샘플 데이터로부터 쉽게 구현될 수 있는 강력하고도 많이 사용되는 형식의 분류기다. 그러나 강한 조건부 독립성으로 인하여 효율이 저하되는 분류 결과를 초래한다. 일반적으로 네이브 베이지안 분류기는 연속성을 가진 특징 데이터의 우도를 처리하기 위해 가우시안 분산을 사용한다. 속성들의 확률밀도는 항상 가우시안 분산에 적합한 것만은 아니다. 또 다른 형식의 분류기는 지도학습을 통해 퍼지 규칙과 퍼지집합을 학습할 수 있는 퍼지신경망이다. 퍼지신경망과 네이브 베이지안 분류기간에는 구조적 유사성을 가지고 있기 때문에 퍼지신경망으로 학습된 분산 그래프를 네이브 베이지안 분류기에 적용하고자 하는 방안이 본 연구의 목적이다. 따라서 네이브 베이지안 분류기에 가우시안 분산 그래프를 사용한 결과와 퍼지 분산 그래프를 사용한 결과를 비교하였다. 이를 위해 leukemia와 colon의 DNA 마이크로어레이 데이터를 적용하여 분류하였다. 네이브 베이지안 분류기에 퍼지 분산 그래프를 사용한 결과 가우시안 분산 그래프를 사용한 결과보다 더 신뢰성이 있음을 보여주었다.

Phase Compensation of Fuzzy Control Systems and Realization of Neuro-fuzzy Compenastors

  • Tanaka, Kazuo;Sano, Manabu
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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    • pp.845-848
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    • 1993
  • This paper proposes a design method of fuzzy phase-lead compensator and its self-learning by neural network. The main feature of the fuzzy phase-lead compensator is to have parameters for effectively compensating phase characteristics of control systems. An important theorem which is related to phase-lead compensation is derived by introducing concept of frequency characteristics. We propose a design procedure of fuzzy phase-lead compensators for linear controlled objects. Furthermore, we realize a neuro-fuzzy compensator for unknown or nonlinear controlled objects by using Widrow-Hoff learning rule.

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자기조정 뉴로-퍼지제어기를 이용한 다지역 전력시스템의 부하주파수 제어 (Load Frequency Control of Multi-area Power System using Auto-tuning Neuro-Fuzzy Controller)

  • 정형환;김상효;주석민;허동렬;이권순
    • 대한전기학회논문지:전력기술부문A
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    • 제49권3호
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    • pp.95-106
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    • 2000
  • The load frequency control of power system is one of important subjects in view of system operation and control. That is even though the rapid load disturbances were applied to the given power system, the stable and reliable power should be supplied to the users, converging unconditionally and rapidly the frequency deviations and the tie-line power flow one on each area into allowable boundary limits. Nonetheless of such needs, if the internal parameter perturbation and the sudden load variation were given, the unstable phenomenal of power system can be often brought out because of the large frequency deviation and the unsuppressible power line one. Therefore, it is desirable to design the robust neuro-fuzzy controller which can stabilize effectively the given power system as soon as possible. In this paper the robust neuro-fuzzy controller was proposed and applied to control of load frequency over multi-area power system. The architecture and algorithm of a designed NFC(Neuro-Fuzzy Controller) were consist of fuzzy controller and neural network for auto tuning of fuzzy controller. The adaptively learned antecedent and consequent parameters of membership functions in fuzzy controller were acquired from the steepest gradient method for error-back propagation algorithm. The performances of the resultant NFC, that is, the steady-state deviations of frequency and tie-line power flow and the related dynamics, were investigated and analyzed in detail by being applied to the load frequency control of multi-area power system, when the perturbations of predetermined internal parameters. Through the simulation results tried variously in this paper for disturbances of internal parameters and external stepwise load stepwise load changes, the superiorities of the proposed NFC in robustness and adaptive rapidity to the conventional controllers were proved.

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적응 퍼지-뉴로 제어기를 이용한 IPMSM 드라이브의 최대토크 제어 (Maximum Torque Control of IPMSM Drive using Adaptive Fuzzy-Neuro Controller)

  • 김도연;고재섭;최정식;정병진;박기태;최정훈;정동화
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2007년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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    • pp.126-128
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    • 2007
  • This paper proposes maximum torque control of IPMSM drive using Adaptive Fuzzy-Neuro controller and artificial neural network(ANN). The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. This paper proposes the analysis results to verify the effectiveness of the Adaptive Fuzzy-Neuro and ANN controller.

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Development of an Adaptive Neuro-Fuzzy Techniques based PD-Model for the Insulation Condition Monitoring and Diagnosis

  • Kim, Y.J.;Lim, J.S.;Park, D.H.;Cho, K.B.
    • E2M - 전기 전자와 첨단 소재
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    • 제11권11호
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    • pp.1-8
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    • 1998
  • This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.

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적응 퍼지-뉴로 제어기의 설계와 응용 (Design & application of adaptive fuzzy-neuro controllers)

  • 강경운;김용민;강훈;전홍태
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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    • pp.710-717
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    • 1993
  • In this paper, we focus upon the design and applications of adaptive fuzzy-neuro controllers. An intelligent control system is proposed by exploiting the merits of two paradigms, a fuzzy logic controller and a neural network, assuming that we can modify in real time the consequential parts of the rulebase with adaptive learning, and that initial fuzzy control rules are established in a temporarily stable region. We choose the structure of fuzzy hypercubes for the fuzzy controller, and utilize the Perceptron learning rule in order to update the fuzzy control rules on-line with the output error. And, the effectiveness and the robustness of this intelligent controller are shown with application of the proposed adaptive fuzzy-neuro controller to control of the cart-pole system.

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가변부하를 갖는 직류 서보 전동기의 속도제어를 위한 뉴로-퍼지 제어기 설계 (Design of Neuro-Fuzzy Controller for Velocity Control of DC Servo Motor with Variable Loads)

  • 김상훈;강영호;남문현;김낙교
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1999년도 하계학술대회 논문집 B
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    • pp.513-515
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    • 1999
  • In this paper, Neuro-Fuzzy controller which has the characteristic of Fuzzy control and artificial Neural Network is designed A fuzzy rule to be applied is selected automatically by the allocated neurons. The neurons correspond to Fuzzy rules which are created by the expert. In order to adaptivity, the more precise modeling is implemented by error back propagation learning of adjusting the link-weight of fuzzy membership function in Neuro-fuzzy controller. The more classified fuzzy rule is used to include the property of Dual mode Method. To test the effectiveness of the algorithm designed above the experiment for DC servo motor with variable load as variable load plant is implementation.

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뉴로-퍼지 제어기를 이용한 교류 서보 전동기의 속도제어 (Speed control of AC Servo Motor with Neuro-Fuzzy Controller)

  • 김종현;김상훈;고봉운;김낙교
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2001년도 하계학술대회 논문집 D
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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.

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교류 서보 전동기의 속도제어를 위한 뉴로-퍼지 관측기설계 (Neuro-Fuzzy Observer Design for Speed control of AC Servo Motor)

  • 반기종;최성대;윤광호;남문현;김낙교
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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    • pp.170-173
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    • 2005
  • This paper presents an Fuzzy-Neuro Observer system for an ac servo motor dirve to track periodic commands using a neuro-fuzzy observer. AC servo motor drive system is rather similar to a linear system. However, the uncertainties, such as machanical parametric variation, external disturbance, uncertainty due to nonideal in transient state. therefore an intelligent control system that isan on-line trained neural network controller with adaptive learning rates.

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