• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.838-847
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• 1994

Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1140-1145
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• 1989

A general dynamic iterative learning control scheme is proposed for a class of nonlinear systems. Relying on stabilizing high-gain feedback loop, it is possible to show the existence of Cauchy sequence of feedforward control input error with iteration numbers, which results in a uniform convergance of system state trajectory to the desired one.

• The Journal of Korean Association of Computer Education
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• v.5 no.3
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• pp.59-67
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• 2002

In this study, we have designed and established a formative evaluation system which provides the web-based corrective feedback in the current situation that the importance of feedback as well as the formative evaluation is getting highlighted in the course of learning and instruction. This system is composed of three types of corrective feedback. The first one is the informations providing feedback by stage. It provides the explanations or the correct answers-related informations for the wrong answered questions by stage and directly. The second one is the error corrective feedback, which provides the informations about the reasons of the errors on each wrong answered question so that the learner can correct his errors. Lastly, the corrective feedback by the accumulated marks shows the learner's total marks and their wrong answered questions to enable the learner to learn for themselves. We analyzed how these three corrective feedback effects on the learning achievement after applying them to learners, and testified the type of the most effective corrective feedback in enhancing the degree of learning achievement.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3434-3439
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• 2015

As a criterion of information theoretic learning, the Euclidean distance (ED) of two error probability distribution functions (minimum ED of error, MEDE) has been adopted in nonlinear (decision feedback, DF) supervised equalizer algorithms and has shown significantly improved performance in severe channel distortion and impulsive noise environments. However, the MEDE-DF algorithm has the problem of heavy computational complexity. In this paper, the recursive ED for MEDE-DF algorithm is derived first, and then the feed-forward and feedback section gradients for weight update are estimated recursively. To prove the effectiveness of the recursive gradient estimation for the MEDE-DF algorithm, the number of multiplications are compared and MSE performance in impulsive noise and underwater communication environments is compared through computer simulation. The ratio of the number of multiplications between the proposed DF and the conventional MEDE-DF algorithm is revealed to be $2(9N+4):2(3N^2+3N)$ for the sample size N with the same MSE learning performance in the impulsive noise and underwater channel environment.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.5
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• pp.55-63
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• 2004

This paper presents neural network based controller using the feedback error loaming technique for a heavy load pointing system. Also the mathematical model was developed to analyze heavy load pointing system. The control scheme consists of a feedforward neural network controller and a fixed-gain feedback controller. This neural network controller is trained so as to make the output of the feedback controller zero. The proposed controller is compared with the conventional PI controller through simulations, and the results show that the pointing accuracy of the proposed control system are improved against the disturbance induced by vehicle running on the bump course.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.327-329
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• 1998

Up-Propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden variables using top-down connections. The inversion process is iterative, utilizing a negative feedback loop that depends on an error signal propagated by bottom-up connections. The error signal is also used to learn the generative model from examples. the algorithm is benchmarked against principal component analysis in experiments on images of handwritten digits.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.71-81
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• 2019

To extend the potential benefits of error, the current study examined factors that affect students' error perception in the classroom. An experimental design was used to measure relations of classroom goal structure, feedback, and social relationships on students' perception of error. A total of 316 fourth-, fifth-, and sixth-grade elementary students participated as part of their regular class curriculum. Self-reported questionnaires were administered to measure students' perception of errors and relationships with teacher and peers, and then students were manipulated by classroom goal structure and feedback. Multiple regression analysis results suggested that students' perception of learning from error was affected mostly by relationships with peers, followed by relationships with teacher and the type of feedback. Students' perception of risk taking for error was also affected by relationships with peers and teacher, followed by the classroom goal structure. However, classroom goal structure and feedback did not affect their perception of thinking about error to improve their learning as well as error strain. These results imply how the classroom climate should be structured to improve perception of errors to improve student's learning.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.443-446
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• 2003

Conventional disturbance rejection methods have to derive the inverse model of a system. However, the inverse model of n nonholonomic system is not unique, because an inverse it changes depending on initial conditions and desired values. A kind of internal model control (IMC) using feedback error learning is discussed for the motion control of nonholonomic mobile robots in this paper, The present method is different from a conventional IMC whose control system consists of an inverse model, a direct model and a filter. The present disturbance rejection method need not use a direct model, where the remaining two elements are composed of the same inverse model based on neural networks.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.431-431
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• 2000

The modeling of 5-bar linkage robot manipulator dynamics by means of a mathematical and neural architecture is presented. Such a model is applicable to the design of a feedforward controller or adjustment of controller parameters. The inverse model consists of two parts: a mathematical part and a compensation part. In the mathematical part, the subsystems of a 5-bar linkage robot manipulator are constructed by applying Kawato's Feedback-Error-Learning method, and trained by given training data. In the compensation part, MLP backpropagation algorithm is used to compensate the unmodeled dynamics. The forward model is realized from the inverse model using the inverse of inertia matrix and the compensation torque is decoupled in the input torque of the forward model. This scheme can use tile mathematical knowledge of the robot manipulator and analogize the robot characteristics. It is shown that the model is reasonable to be used for design and initial gain tuning of a controller.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.1
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• pp.79-86
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• 2012

This paper proposes PID and RIC (Robust Internal-loop Compensator) based motion controller using dual learning algorithm for position control of linear synchronous motor respectively. Its gains are auto-tuned by using two learning algorithms, reinforcement learning and neural network. The feedback controller gains are tuned by reinforcement learning, and then the feedforward controller gains are tuned by neural network. Experiments prove the validity of dual learning algorithm. The RIC controller has better performance than does the PID-feedforward controller in reducing tracking error and disturbance rejection. Neural network shows its ability to decrease tracking error and to reject disturbance in the stop range of the target position and home.