• Title/Summary/Keyword: neural learning scheme

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Design of Multi-Dynamic Neuro-Fuzzy Controller for Dynamic Systems Control (동적시스템 제어를 위한 다단동적 뉴로-퍼지 제어기 설계)

  • Cho, Hyun-Seob;Min, Jin-Kyoung
    • Proceedings of the KAIS Fall Conference
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    • 2007.05a
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    • pp.150-153
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    • 2007
  • The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

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New learning algorithm to solve the inverse optimization problems

  • Aoyama, Tomoo
    • 제어로봇시스템학회:학술대회논문집
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    • 2002.10a
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    • pp.42.2-42
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    • 2002
  • We discuss a neural network solver for the inverse optimization problem. The problem is that find functional relations between input and output data, which are include defects. Finding the relations, predictions of the defect parts are also required. The part of finding the defects in the input data is an inverse problem . We consider the meanings to solve the problem on the neural network system at first. Next, we consider the network structure of the system, the learning scheme of the network, and at last, examine the precision on the numerical calculations. In the paper, we proposed the high-precision learning method for plural three-layer neural network system that is series-connect...

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Adaptive Control of Non-linearity Dynamic System using DNU (DNU에 의한 비선형 동적시스템의 적응제어)

  • Cho, Hyeon-Seob;Kim, Hee-Sook
    • Proceedings of the KIEE Conference
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    • 1998.11b
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    • pp.533-536
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    • 1998
  • The intent of this paper is to describe a neural network structure called dynamic neural processor(DNP), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the DNP, are described. Computer simulations are provided to demonstrate the effectiveness of the proposed learning using the DNP.

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Orientation Control of Mobile Robot Using Fuzzy-Neural Control Technique (퍼지-뉴럴 제어기법에 의한 이동형 로봇의 자세 제어)

  • 김종수
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 1997.10a
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    • pp.82-87
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    • 1997
  • This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

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Nonlinear system control by use of neural networks

  • Zhang, Ping;Sankai, Yoshiyuki;Ohta, Michio
    • 제어로봇시스템학회:학술대회논문집
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    • 1994.10a
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    • pp.411-415
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    • 1994
  • An adaptive learning control scheme by use of multilayer neural networks for compensating for uncertainties in nonlinear dynamic system is examined. Multilayer neural networks are introduced to map the uncertainties in nonlinear dynamics and perform nonlinear state feedback. Parameters of neural networks are adjusted by conventional back-propagation algorithms modified with the projection operation. Effectiveness of the proposed scheme for tracking control are demonstrated through computer simulations.

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A Fuzzy Neural Network: Structure and Learning

  • Figueiredo, M.;Gomide, F.;Pedrycz, W.
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1993.06a
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    • pp.1171-1174
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    • 1993
  • A promising approach to get the benefits of neural networks and fuzzy logic is to combine them into an integrated system to merge the computational power of neural networks and the representation and reasoning properties of fuzzy logic. In this context, this paper presents a fuzzy neural network which is able to code fuzzy knowledge in the form of it-then rules in its structure. The network also provides an efficient structure not only to code knowledge, but also to support fuzzy reasoning and information processing. A learning scheme is also derived for a class of membership functions.

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Input Signal Estimation About Controller Using Neural Networks (신경망을 이용한 제어기에 인가된 입력 신호의 추정)

  • Son Jun-Hyeok;Seo Bo-Hyeok
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.54 no.8
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    • pp.495-497
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    • 2005
  • Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

Input signal estimation about controller using neural networks (신경망을 이용한 제어기에 인가된 입력 신호의 추정)

  • Son, Jun-Hyeok;Seo, Bo-Hyeok
    • Proceedings of the KIEE Conference
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    • 2005.05a
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    • pp.18-20
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    • 2005
  • Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

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Evolvable Neural Networks for Time Series Prediction with Adaptive Learning Interval

  • Seo, Sang-Wook;Lee, Dong-Wook;Sim, Kwee-Bo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.8 no.1
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    • pp.31-36
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    • 2008
  • This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

Evolvable Neural Networks for Time Series Prediction with Adaptive Learning Interval

  • Lee, Dong-Wook;Kong, Seong-G;Sim, Kwee-Bo
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.920-924
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    • 2005
  • This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

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