• Title/Summary/Keyword: Aritificial Neural Network

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A variable PID controller for robots using evolution strategy and neural network (Evolution strategy와 신경회로망에 의한 로봇의 가변 PID제어기)

  • 최상구;김현식;최영규
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.1585-1588
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    • 1997
  • In this paper, divide total workspace of robot manipulator into several subspaces and construct PID controller ineach subspace. Using EvolutionSTrategy we optimize the gains of PID controller in each subspace. But the gains may have a large difference on the boundary of subspaces, which can cause bad oscillatory performance. So we use Aritificial Neural Network to have continuous gain curves htrough the entire subspaces. Simualtion results show that the proposed method is quite useful.

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Vehicle Load Analysis using Bridge-Weigh-in-Motion System in a Cable Stayed Bridge (BWIM 시스템을 사용한 사장교의 차량하중 분석)

  • Park, Min-Seok;Lee, Jung-Whee;Kim, Sung-Kon;Jo, Byung-Wan
    • Journal of the Earthquake Engineering Society of Korea
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    • v.10 no.6 s.52
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    • pp.1-8
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    • 2006
  • This paper describes the procedures developing the algorithm for analyzing signals acquired from the Bridge Weigh-in-Motion (BWIM) system installed in Seohae Bridge as a part of the bridge monitoring system. Through the analysis procedure, information about heavy traffics such as weight, speed, and number of axles are attempted to be extracted from time domain strain data of the BWIM system. One of numerous pattern recognition techniques, artificial neural network (ANN) is employed since it can effectively include dynamic effects, bridge-vehicle interaction, etc. A number of vehicle running experiments with sufficient load cases are executed to acquire training and/or test set of ANN. Extracted traffic information can be utilized for developing quantitative database of loading effect. Also, it can contribute to estimate fatigue lift or current health condition, and design truck can be revised based on the database reflecting recent trend of traffic.