• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.2
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• pp.464-470
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• 2004

We propose an enhanced backpropagation algorithm by auto-tuning of learning rate using fuzzy control system for performance improvement of backpropagation algorithm. We propose two methods, which improve local minima and loaming times problem. First, if absolute value of difference between target and actual output value is smaller than $\varepsilon$ or the same, we define it as correctness. And if bigger than $\varepsilon$, we define it as incorrectness. Second, instead of choosing a fixed learning rate, the proposed method is used to dynamically adjust learning rate using fuzzy control system. The inputs of fuzzy control system are number of correctness and incorrectness, and the output is the Loaming rate. For the evaluation of performance of the proposed method, we applied the XOR problem and numeral patterns classification The experimentation results showed that the proposed method has improved the performance compared to the conventional backpropagatiot the backpropagation with momentum, and the Jacob's delta-bar-delta method.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.49.4-49
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• 2001

Digit recognition based on backpropagation neural networks, as an important application of pattern recognition, was attracted much attention. Although it has the advantages of parallel calculation, high error-tolerance, and learning capability, better recognition effects can only be achieved with some specific fixed format input of the digit image. Therefore, digit image preprocessing ability directly affects the accuracy of recognition. Here using Matlab software, the digit image was enhanced by resizing and neutral-rotating the extracted digit image, which improved the digit recognition capability of the backpropagation neural network under practical conditions. This method may also be helpful for recognition of other patterns with backpropagation neural networks.

• Journal of Welding and Joining
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• v.21 no.1
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• pp.80-86
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• 2003

This study develops a system for effective prediction of residual stresses by the backpropagation algorithm using the neural network. To achieve this goal, a series of experiments were carried out to and measured the residual stresses using the sectional method. With the experimental results, the optional control algorithms using a neural network could be developed in order to reduce the effect of the external disturbances during GMA welding processes. Then the results obtained from this study were compared between the measured and calculated results, weld guality might be controlled by the neural network based on backpropagation algorithm.. This system can not only help to understand the interaction between the process parameters and residual stress, but also improve the quantity control for welded structures.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.484-490
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• 2000

The abjective of the study is the development of the system for effective prediction of residual stresses using the backpropagation algorithm from the neural network. To achieve this goal, the series experiment were carried out and measured the residual stresses using the sectional method. Using the experimental results, the optional control algorithms using a neural network should be developed in order to reduce the effect of the external disturbances on during GMA welding processes. Then the results obtained from this study were compared between the measured and calculated results, the neural network based on backpropagation algorithm might be controlled weld quality. This system can not only help to understand the interaction between the process parameters and residual stress, but also improve the quantity control for welded structures.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.644-649
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• 2012

This paper proposes the gait phase classifier using backpropagation neural networks method which uses the angle of lower body's joints and ground reaction force as input signals. The classification of a gait phase is useful to understand the gait characteristics of pathologic gait and to control the gait rehabilitation systems. The classifier categorizes a gait cycle as 7 phases which are commonly used to classify the sub-phases of the gait in the literature. We verify the efficiency of the proposed method through experiments.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.754-756
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• 2004

The ultrasonic piezo motor is a new type motor that has an excellent performance and many useful features that electromagnetic motors do not have. But, it suffers from severe system non-linearities and parameter variations especially during speed control. Therefore, it is difficult to accomplish satisfactory control performance by using the conventional PID controller. In this paper, to achieve the precise control, we analyzed response time & change with a driving time, and proposed PD controller combined with neural network. The backpropagation algorithm is used to train a given trajectory. The effectiveness of the used method is confirmed by experiments. The effectiveness of the used method is confirmed by experiments using the ultrasonic motor made in Korea.

• Journal of the Korean Society of Safety
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• v.16 no.4
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• pp.58-64
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• 2001

In this study, it is developed that the system for effective prediction of residual stresses by the back-propagation algorithm using the neural network. To achieve This goal, the series experiment were carried out and measured the residual stresses using the sectional method. Using the experimental results, the optional control algorithms using a neural network should be developed in order to reduce the effect of the external disturbances during GMA welding processes. Then the results obtained from this study were compared between the measured and calculated results, weld guality might be controlled by the neural network based on backpropagation algorithm. This system can no only help to understand the interaction between the process parameters and residual stress, but also improve the quantity control for welded structures.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.4
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• pp.11-19
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• 1996

Recently, fuzzy and neural network techniques have been successfully applied to control of complex and ill-defined system in a wide variety of areas, such as robot, water purification, automatic train operation system and automatic container crane operation system, etc. In this paper, we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feedforward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand, feedforward neural networks provide salient features, such as learning and parallelism. In the proposed neuro-fuzzy controller, the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error backpropagation algorithm as a learning rule, while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally, the effectiveness of the proposed controller is verified through computer simulation of an inverted pendulum system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.236-241
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• 2007

This paper presents an adaptive controller using chaotic dynamic neural networks(CDNN) for nonlinear dynamic system. A new dynamic backpropagation learning method of the proposed chaotic dynamic neural networks is developed for efficient learning, and this learning method includes the convergence for improving the stability of chaotic neural networks. The proposed CDNN is applied to the system identification of chaotic system and the adaptive controller. The simulation results show good performances in the identification of Lorenz equation and the adaptive control of nonlinear system, since the CDNN has the fast learning characteristics and the robust adaptability to nonlinear dynamic system.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.6
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• pp.89-95
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• 2000

In order to achieve effective prediction of residual stresses, the series experiment were carried out and the residual stresses were measured using the backgpropagation algorithm from the neural network and the sectional method. Using the experimental results, the optimal control algorithms using a neural network should be developed in order to reduce the effect of the external disturbances on residual stresses during GMA welding processes. The results obtained from the comparison between the measured and calculated results, showed that the neural network based on backpropagation algorithm can be sued in order to control weld quality. This system can not only help to understand the interaction between the process parameters and residual stress, but also, improve the quantity control for welded structures. The development of the system is goal in this study.