• Title/Summary/Keyword: CMAC learning controller

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Adaptive Control of Nonlinear System Using CMAC (CMAC를 이용한 비선형 시스템의 적응 제어)

  • Ahn, Dae-Chan;Lee, Young-Seog;Kim, Sung-Sik;Seo, Bo-Hyeok
    • Proceedings of the KIEE Conference
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    • 1997.07b
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    • pp.708-710
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    • 1997
  • In this paper, an adaptive control scheme is proposed for slowly time-varying discrete-time nonlinear dynamic system. CMAC networks are employed to identify system from input-output data and to construct the controller based on this identifer. All of learning procedures are performed on-line. Computer simulation result shows the usefulness of the proposed scheme.

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Sensitivity Property of Generalized CMAC Neural Network

  • Kim, Dong-Hyawn;Lee, In-Won
    • Computational Structural Engineering : An International Journal
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    • v.3 no.1
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    • pp.39-47
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    • 2003
  • Generalized CMAC (GCMAC) is a type of neural network known to be fast in learning. The network may be useful in structural engineering applications such as the identification and the control of structures. The derivatives of a trained GCMAC is relatively poor in accuracy. Therefore to improve the accuracy, a new algorithm is proposed. If GCMAC is directly differentiated, the accuracy of the derivative is not satisfactory. This is due to the quantization of input space and the shape of basis function used. Using the periodicity of the predicted output by GCMAC, the derivative can be improved to the extent of having almost no error. Numerical examples are considered to show the accuracy of the proposed algorithm.

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Design of a robot learning controller using associative mapping memory (연관사상 메모리를 이용한 로봇 머니퓰레이터의 학습제어기 설계)

  • 정재욱;국태용;이택종
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.936-939
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    • 1996
  • In this paper, two specially designed associative mapping memories, called Associative Mapping Elements(AME) and Multiple-Digit Overlapping AME(MDO-AME), are presented for learning of nonlinear functions including kinematics and dynamics of robot manipulators. The proposed associative mapping memories consist of associative mapping rules(AMR) and weight update rules(WUR) which guarantee generalization and specialization of input-output relationship of learned nonlinear functions. Two simulation results, one for supervised learning and the other for unsupervised learning, are given to demonstrate the effectiveness of the proposed associative mapping memories.

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Composite adaptive neural network controller for nonlinear systems (비선형 시스템제어를 위한 복합적응 신경회로망)

  • 김효규;오세영;김성권
    • 제어로봇시스템학회:학술대회논문집
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    • 1993.10a
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    • pp.14-19
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    • 1993
  • In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.

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On Learning and Structure of Cerebellum Model Linear Associator Network(I) -Analysis & Development of Learning Algorithm- (소뇌모델 선형조합 신경망의 구조 및 학습기능 연구(I) -분석 및 학습 알고리즘 개발-)

  • Hwang, H.;Baek, P.K.
    • Journal of Biosystems Engineering
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    • v.15 no.3
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    • pp.186-198
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    • 1990
  • 인간 소뇌의 구조와 기능을 간략하게 수학적으로 모델링하여 입력에 따른 시스템의 적정 출력을 학습에 의한 적응 제어 방식으로 추출해 내는 소뇌모델 대수제어기(CMAC : Cerebellar Model Arithmetic Controller)가 제안되었다. 본 논문에서는 연구개발된 기존 신경회로망과의 비교 분석에 의거하여, 소뇌모델 대수제어기 대신 네트의 특성에 따라 소뇌모델 선형조합 신경망(CMLAN : Cerebellum Model Linear Associator Network)이라 하였다. 소뇌모델 선형조합 신경망은 시스템의 제어 함수치를 결정하는 데 있어, 기존의 제어방식이 시스템의 모델링을 기초로 하여 알고리즘에 의한 수치해석적 또는 분석적 기법으로 모델 해를 산출하는 것과 달리, 학습을 통하여 저장되는 분산기억 소자들의 함수치를 선형적으로 조합함으로써 시스템의 입출력을 결정한다. 분산기억 소자로의 함수치 산정 및 저장은 소뇌모델 선형조합 신경망이 갖는 고유의 구조적 상태공간 매핑(State Space Mapping)과 델타규칙(Delta Rule)에 의거한 시스템의 입출력 상태함수의 학습으로써 수행된다. 본 논문을 통하여 소뇌모델 선형조합신경망의 구조적 특성, 학습 성질과 상태공간 설정 및 시스템의 수렴성을 규명하였다. 또한 기존의 최대 편차수정 학습 알고리즘이 갖는 비능률성 및 적용 제한성을 극복한 효율적 학습 알고리즘들을 제시하였다. 언급한 신경망의 특성 및 제안된 학습 알고리즘들의 능률성을 다양한 학습이득(Learning Gain)하에서 비선형 함수를 컴퓨터로 모의 시험하여 예시하였다.

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