• Title/Summary/Keyword: CMAC

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CMAC Controller with Adaptive Critic Learning for Cart-Pole System (운반차-막대 시스템을 위한 적응비평학습에 의한 CMAC 제어계)

  • 권성규
    • Journal of the Korean Institute of Intelligent Systems
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    • v.10 no.5
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    • pp.466-477
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    • 2000
  • For developing a CMAC-based adaptive critic learning system to control the cart-pole system, various papers including neural network based learning control schemes as well as an adaptive critic learning algorithm with Adaptive Search Element are reviewed and the adaptive critic learning algorithm for the ASE is integrated into a CMAC controller. Also, quantization problems involved in integrating CMAC into ASE system are studied. By comparing the learning speed of the CMAC system with that of the ASE system and by considering the learning genemlization of the CMAC system with the adaptive critic learning, the applicability of the adaptive critic learning algorithm to CMAC is discussed.

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Neighborhood Sequential Training Technique for CMAC (CMAC을 위한 이웃간訓鍊 方法)

  • 권성규
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.16 no.10
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    • pp.1816-1823
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    • 1992
  • In order to develop general CMAC training technique applicable to any CMAC, characteristics of CMAC learning algorithm and training problems of CMAC are studied. Neighborhood Sequential Training technique which is general and free fro CMAC learning interference is proposed. The technique is used to generate mathematical functions and found to be effective.

CMAC Neuro-Fuzzy Design for Color Calibration (컬러재현을 위한 CMAC의 뉴로퍼지 설계)

  • 이철희;변오성;문성룡;임기영
    • Journal of the Korean Institute of Intelligent Systems
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    • v.11 no.4
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    • pp.331-335
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    • 2001
  • Cl\iAC model was proposed by Albus [6J to formulate the processing characteristics of the human cerebellum. Instead of the global weight updating scheme used in the back propagation, CMAC use the local weight updating scheme. Therefore, CMAC have the advantage of fast learning and high convergence rate. In this paper, simulate Color Calibration by CMAC in color images and design hardware by VHDL-base high-level synthesis.

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Robust Tracking Control of a Flexible Joint Robot System using a CMAC Neural Network Disturbance Observer (CMAC 신경망 외란관측기를 이용한 유연관절 로봇의 강인 추적제어)

  • 김은태
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.40 no.5
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    • pp.299-307
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    • 2003
  • The local structure of CMAC neural networks (NN) results in better and faster controllers for nonlinear dynamical systems. In this paper, we propose a CMAC NN-based disturbance observer and its corresponding controller for a flexible joint robot. The CMAC NN-based disturbance observer compensates for the parametric uncertainties and the external disturbances throughout the entire mechanical system. Finally, a simulation result is given to demonstrate the effectiveness of proposed design method's robust tracking performance.

Design for CMAC Neural Network Speed Controller of DC Motor by Digital Simulations (디지털 시뮬레이션에 의한 CMAC 신경망 직류전동기 속도 제어기 설계)

  • 최광호;조용범
    • The Transactions of the Korean Institute of Power Electronics
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    • v.6 no.3
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    • pp.273-281
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    • 2001
  • In this paper, we propose a CMAC(Cerebellar Model Articulation Controller) neural network for controlling a non-linear system. CMAC is a neural network that models the human cerebellum. CMAC uses a table look-up method to resolve the complex non-linear system instead of numerical calculation method. It is very fast learn compared with other neural networks. It does not need a calculation time to generate control signals. The simulation results show that the proposed CMAC controllers for a simple non-linear function and a DC Motor speed control reduce tracking errors and improve the stability of its learning controllers. The validity of the proposed CMAC controller is also proved by the real-time tension control.

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CMAC Learning Controller Implementation With Multiple Sampling Rate: An Inverted Pendulum Example (다중 샘플링 타임을 갖는 CMAC 학습 제어기 실현: 역진자 제어)

  • Lee, Byoung-Soo
    • Journal of Institute of Control, Robotics and Systems
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    • v.13 no.4
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    • pp.279-285
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    • 2007
  • The objective of the research is two fold. The first is to design and propose a stable and robust learning control algorithm. The controller is CMAC Learning Controller which consists of a model-based controller, such as LQR or PID, as a reference control and a CMAC. The second objective is to implement a reference control and CMAC at two different sampling rates. Generally, a conventional controller is designed based on a mathematical plant model. However, increasing complexity of the plant and accuracy requirement on mathematical models nearly prohibits the application of the conventional controller design approach. To avoid inherent complexity and unavoidable uncertainty in modeling, biology mimetic methods have been developed. One of such attempts is Cerebellar Model Articulation Computer(CMAC) developed by Albus. CMAC has two main disadvantages. The first disadvantage of CMAC is increasing memory requirement with increasing number of input variables and with increasing accuracy demand. The memory needs can be solved with cheap memories due to recent development of new memory technology. The second disadvantage is a demand for processing powers which could be an obstacle especially when CMAC should be implemented in real-time. To overcome the disadvantages of CMAC, we propose CMAC learning controller with multiple sampling rates. With this approach a conventional controller which is a reference to CMAC at high enough sampling rate but CMAC runs at the processor's unoccupied time. To show efficiency of the proposed method, an inverted pendulum controller is designed and implemented. We also demonstrate it's possibility as an industrial control solution and robustness against a modeling uncertainty.

Learning Control Based on CMAC Neural Networks (CMAC 신경회로망을 기반으로 한 학습제어)

  • Yoo, J.J.;Chung, T.J.;Choi, J.S.
    • Electronics and Telecommunications Trends
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    • v.8 no.3
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    • pp.11-20
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    • 1993
  • CMAC 신경회로망은 다차원 비선형 함수를 학습을 통하여 발생되는 많고 복잡한 데이터들을 퍼셉트론과 같이 집합시켜 메모리를 구성하고 처리하는 분야이다. 일반적으로 학습알고리즘은 소수의 반복으로써 수렴한다. 본고에서는 CMAC의 메카니즘 및 CMAC의 특성을 기술하고, CMAC의 학습가능성을 예시하였다. CMAC의 학습성능을 시험하기 위해서 3관절 로봇의 squatting 문제에 적용하였다.

Optimal Design of CMAC network Using Evolution Strategies (진화 스트레티지를 이용한 CMAC 망 최적 설계)

  • 이선우;김상권;김종환
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1997.11a
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    • pp.271-274
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    • 1997
  • This paper presents the optimization technique for design of a CMAC network by using an evolution strategies(ES). The proposed technique is designed to find the optimal parameters of a CMAC network, which can minimize the learning error between the desired output and the CMAC network's as well as the number of memory used in the CMAC network. Computer simulations demonstrate the effectiveness of the proposed design method.

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CMAC Neuro-Fuzzy Design for Color Calibration (컬러재현을 위한 CMAC의 뉴로퍼지 설계)

  • 이철희;변오성;문성룡;임기영
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2001.05a
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    • pp.97-100
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    • 2001
  • CMAC model was proposed by Albus [6] to formulate the processing characteristics of the human cerebellum. Instead of the global weight updating scheme used in the back propagation, CMAC use the local weight updating scheme. Therefore, CMAC have the advantage of fast learning and high convergence rate. In this paper, simulate Color Calibration by CMAC in color images and design hardware by VHDL-base high-level synthesis.

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An Effective Memory Mapping Function for CMAC Controller (CMAC 제어기를 위한 효과적인 메모리 매핑 함수)

  • Kwon, H.Y.;Bien, Z.;Suh, I.H.
    • Proceedings of the KIEE Conference
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    • 1989.11a
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    • pp.488-493
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    • 1989
  • In this paper, the structure of CMAC address mapping is first revisited, and the address hashing function and the random mapping is discussed in the conventional CMAC implementation. Then the effective size of CMAC memory is derived from the modulus property of the CMAC address vector, and a new hashing function for the effective memory mapping is proposed for a CMAC implementation with feasible memory size and no troublesome random mapping. Finally, the performance of the conventional CMAC learning algorithm and that of the proposed new CMAC scheme arc compared via simulations.

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