• Title/Summary/Keyword: Gradient Descent Algorithm

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Comparison of Different Schemes for Speed Sensorless Control of Induction Motor Drives by Neural Network (유도전동기의 속도 센서리스 제어를 위한 신경회로망 알고리즘의 추정 특성 비교)

  • 이경훈;국윤상;김윤호;최원범
    • Proceedings of the KIPE Conference
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    • 1999.07a
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    • pp.526-530
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    • 1999
  • This paper presents a newly developed speed sensorless drive using Neural Network algorithm. Neural Network algorithm can be divided into three categories. In the first one, a Back Propagation-based NN algorithm is well-known to gradient descent method. In the second scheme, a Extended Kalman Filter-based NN algorithm has just the time varying learning rate. In the last scheme, a Recursive Least Square-based NN algorithm is faster and more stable than the classical back-propagation algorithm for training multilayer perceptrons. The number of iterations required to converge and the mean-squared error between the desired and actual outputs is compared with respect to each method. The theoretical analysis and experimental results are discussed.

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Fuzzy Gain Scheduling of Velocity PI Controller with Intelligent Learning Algorithm for Reactor Control

  • Kim, Dong-Yun;Seong, Poong-Hyun
    • Proceedings of the Korean Nuclear Society Conference
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    • 1996.11a
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    • pp.73-78
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    • 1996
  • In this study, we proposed a fuzzy gain scheduler with intelligent learning algorithm for a reactor control. In the proposed algorithm, we used the gradient descent method to learn the rule bases of a fuzzy algorithm. These rule bases are learned toward minimizing an objective function, which is called a performance cost function. The objective of fuzzy gain scheduler with intelligent learning algorithm is the generation of adequate gains, which minimize the error of system. The condition of every plant is generally changed as time gose. That is, the initial gains obtained through the analysis of system are no longer suitable for the changed plant. And we need to set new gains, which minimize the error stemmed from changing the condition of a plant. In this paper, we applied this strategy for reactor control of nuclear power plant (NPP), and the results were compared with those of a simple PI controller, which has fixed gains. As a result, it was shown that the proposed algorithm was superior to the simple PI controller.

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Implementation of Speed-Sensorless Induction Motor Drives with RLS Algorithm (RLS 알로리즘을 이용한 유도전동기의 속도 센서리스 운전)

  • 김윤호;국윤상
    • Proceedings of the KIPE Conference
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    • 1998.07a
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    • pp.384-387
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    • 1998
  • This paper presents a newly developed speed sensorless drive using RLS(Recursive Least Squares) based on Neural Network Training Algorithm. The proposed algorithm based on the RLS has just the time-varying learning rate, while the well-known back-propagation (or generalized delta rule) algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the back-propagation algorithm. The RLS based on NN is used to adjust the motor speed so that the neural model output follows the desired trajectory. This mechanism forces the estimated speed to follow precisely the actual motor speed. In this paper, a flux estimation strategy using filter concept is discussed. The theoretical analysis and experimental results to verify the effectiveness of the proposed analysis and the proposed control strategy are described.

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Water Flowing and Shaking Optimization

  • Jung, Sung-Hoon
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.12 no.2
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    • pp.173-180
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    • 2012
  • This paper proposes a novel optimization algorithm inspired by water flowing and shaking behaviors in a vessel. Water drops in our algorithm flow to the gradient descent direction and are sometimes shaken for getting out of local optimum areas when most water drops fall in local optimum areas. These flowing and shaking operations allow our algorithm to quickly approach to the global optimum without staying in local optimum areas. We experimented our algorithm with four function optimization problems and compared its results with those of particle swarm optimization. Experimental results showed that our algorithm is superior to the particle swarm optimization algorithm in terms of the speed and success ratio of finding the global optimum.

Hierarchical Height Reconstruction of Object from Shading Using Genetic Algorithm (유전자 알고리즘을 이용한 영상으로부터의 물체높이의 계층적 재구성)

  • Ahn, Eun-Young;Cho, Hyung-Je
    • The Transactions of the Korea Information Processing Society
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    • v.6 no.12
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    • pp.3703-3709
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    • 1999
  • We propose a new approach to reconstruct the surface shape of an object from a shaded image. We use genetic algorithm instead of gradient descent algorithm which is apt to take to local minima and also proposes genetic representation and suitable genetic operators for manipulating 2-D image. And for more effective execution, we suggest hierarchical process to reconstruct minutely the surface of an object after coarse and global reconstruction. A modified Lambertian illumination model including the distance factor was herein adopted to get more reasonable result and an experiment was performed with synthesized and real images to demonstrate the devised method, of which results show the usefulness of our method.

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A new training method of multilayer neural networks using a hybrid of backpropagation algorithm and dynamic tunneling system (후향전파 알고리즘과 동적터널링 시스템을 조합한 다층신경망의 새로운 학습방법)

  • 조용현
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.33B no.4
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    • pp.201-208
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    • 1996
  • This paper proposes an efficient method for improving the training performance of the neural network using a hybrid of backpropagation algorithm and dynamic tunneling system.The backpropagation algorithm, which is the fast gradient descent method, is applied for high-speed optimization. The dynamic tunneling system, which is the deterministic method iwth a tunneling phenomenone, is applied for blobal optimization. Converging to the local minima by using the backpropagation algorithm, the approximate initial point for escaping the local minima is estimated by the pattern classification, and the simulation results show that the performance of proposed method is superior th that of backpropagation algorithm with randomized initial point settings.

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A on-line learning algorithm for recurrent neural networks using variational method (변분법을 이용한 재귀신경망의 온라인 학습)

  • Oh, Oh, Won-Geun;Suh, Suh, Byung-Suhl
    • Journal of Institute of Control, Robotics and Systems
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    • v.2 no.1
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    • pp.21-25
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    • 1996
  • In this paper we suggest a general purpose RNN training algorithm which is derived on the optimal control concepts and variational methods. First, learning is regared as an optimal control problem, then using the variational methods we obtain optimal weights which are given by a two-point boundary-value problem. Finally, the modified gradient descent algorithm is applied to RNN for on-line training. This algorithm is intended to be used on learning complex dynamic mappings between time varing I/O data. It is useful for nonlinear control, identification, and signal processing application of RNN because its storage requirement is not high and on-line learning is possible. Simulation results for a nonlinear plant identification are illustrated.

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Simultaneous optimization method of feature transformation and weighting for artificial neural networks using genetic algorithm : Application to Korean stock market

  • Kim, Kyoung-jae;Ingoo Han
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 1999.10a
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    • pp.323-335
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    • 1999
  • In this paper, we propose a new hybrid model of artificial neural networks(ANNs) and genetic algorithm (GA) to optimal feature transformation and feature weighting. Previous research proposed several variants of hybrid ANNs and GA models including feature weighting, feature subset selection and network structure optimization. Among the vast majority of these studies, however, ANNs did not learn the patterns of data well, because they employed GA for simple use. In this study, we incorporate GA in a simultaneous manner to improve the learning and generalization ability of ANNs. In this study, GA plays role to optimize feature weighting and feature transformation simultaneously. Globally optimized feature weighting overcome the well-known limitations of gradient descent algorithm and globally optimized feature transformation also reduce the dimensionality of the feature space and eliminate irrelevant factors in modeling ANNs. By this procedure, we can improve the performance and enhance the generalisability of ANNs.

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Application of Self-Organizing Fuzzy Logic Controller to Nuclear Steam Generator Level Control

  • Park, Gee-Yong;Park, Jae-Chang;Kim, Chang-Hwoi;Kim, Jung-So;Jung, Chul-Hwan;Seong, Poong-Hyun
    • Proceedings of the Korean Nuclear Society Conference
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    • 1996.11a
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    • pp.85-90
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    • 1996
  • In this paper, the self-organizing fuzzy logic controller is developed for water level control of steam generator. In comparison with conventional fuzzy logic controllers, this controller performs control task with no control rules at initial and creates control rules as control behavior goes on, and also modifies its control structure when uncertain disturbance is suspected. Selected parameters in the fuzzy logic controller are updated on-line by the gradient descent loaming algorithm based on the performance cost function. This control algorithm is applied to water level control of steam generator model developed by Lee, et al. The computer simulation results confirm good performance of this control algorithm in all power ranges. This control algorithm can be expected to be used for automatic control of feedwater control system in the nuclear power plant with digital instrumentation and control systems.

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Fuzzy-Sliding Mode Control of Polishing Robot Based on Genetic Algorithm

  • Go, Seok-Jo;Lee, Min-Cheol;Park, Min-Kyu
    • 제어로봇시스템학회:학술대회논문집
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    • 1999.10a
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    • pp.173-176
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    • 1999
  • This paper shows a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a Polishing robot. Using this method, the number of inference rules and the shape of membership functions are determined by the genetic algorithm. The fuzzy outputs of the consequent part are derived by the gradient descent method. Also, it is guaranteed that .the selected solution become the global optimal solution by optimizing the Akaike's information criterion expressing the quality of the inference rules. It is shown by simulations that the method of fuzzy inference by the genetic algorithm provides better learning capability than the trial and error method.

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