• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.4
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• pp.109-119
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• 1999

Switched Reluctance Motor(SRM) have been expanding gradually their awlications in the variable speed drives due to their relatively low cost, simple and robust structure, controllability and high efficiency. In this paper neural network theory is used to detemrine fuzzy-neural network controller's membership ftmctions and fuzzy rules. In addition neural network emulator is used to emulate forward dynamics of SRM and to get error signal at fuzzy-neural controller output layer. Error signal is backpropagated through neural network emulator. The backpropagated error of emulator offers the path which reforms the fuzzy-neural network controller's mmbership ftmctions and fuzzy rules. 32bit Digital Signal Processor(TMS320C31) was used to achieve the high speed control and to realize the fuzzy-neural control algorithm. Simulation and experimental results show that in the case of load variation the proposed control rrethcd was superior to a conventional rrethod in the respect of speed response.sponse.

• Journal of Multimedia Information System
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• v.2 no.1
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• pp.179-186
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• 2015

This paper describes the application of a simulated annealing to a random signal-based learning. The simulated annealing is used to generate the reinforcement signal which is used in the random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural network. It is poor at hill-climbing, whereas simulated annealing has an ability of probabilistic hill-climbing. Therefore, hybridizing a random signal-based learning with the simulated annealing can produce better performance than before. The validity of the proposed algorithm is confirmed by applying it to two different examples. One is finding the minimum of the nonlinear function. And the other is the optimization of fuzzy control rules using inverted pendulum.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.132-137
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• 2004

In this paper, the adaptive noise canceler using neural network with backpropagation is designed. The adaptive noise canceler using the least mean square algorithm has the large correlativity of the reference signal. The performance of the adaptive noise canceler shows the limitation when the information signal is relatively small to the noise. The system proposed in this paper plays an important role in denoising these signals. In addition, the experiments are carried out to analyze the effects of the number of hidden layers and nodes about the system. The performance of the proposed adaptive noise canceler is compared with that of the system which is used the least mean square algorithm.

• Journal of Welding and Joining
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• v.16 no.3
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• pp.102-110
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• 1998

The horizontal fillet welding is prevalently used in heavy and ship building industries to fabricate the large scale structures. A deep understanding of the horizontal fillet welding process is restricted, because the phenomena occurring in welding are very complex and highly non-linear characteristics. To achieve the satisfactory weld bead geometry in robot welding system, the seam tracking algorithm should be reliable. The number of seam tracker was developed for arc welding automation by now. Among these seam tracker, the arc sensor is prevalently used in industrial robot welding system because of its low cost and flexibility. However, the accuracy of arc sensor would be decreased due to the electrical noise and metal transfer. In this study, the signal processing algorithm based on the neural network was implemented to enhance the reliability of measured welding current signals. Moreover, the seam tracking algorithm in conjunction with the signal processing algorithm was implemented to trace the center of weld line. It was revealed that the neural network could be effectively used to predict the welding current signal at the end of weaving.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.769-771
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• 2000

In this paper, pattern recognition scheme for EMG signal using artificial neural network is proposed. For manipulating ability, the movements of human arm are classified into several categories EMG signals of appropriate muscles are collected during arm movement. Patterns of EMG signals of each movement are recognized as follows: 1) The features of each EMG signal are extracted. 2) With these features, the neural network is trained by using feedforward error back-propagation (FFEBP) algorithm. The results show that the arm movements can be classified with EMG signals at high accuracy.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.7
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• pp.44-54
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• 1996

In DS/SS systems it is necessary to synchronize the locally generated despreading signal with the received spreading signal to demodulate the received signal. The synch process between the two signals is usually accomplished in two steps : first acquisition then tracking. In this paper, an acquisition system aided by the neural network is proposed for the rapid and exact acquisition in DS/SS. the neural netowrk is composed o fthree-layered perpecptrons and trained by the backpropagation algorithm. The performance of the proposed system is analyzed and compared with ones of conventional systems using the sequential estimation technique under an additive while gaussian noisy channel. In all of th econsidered simulations, the proposed system outperforms conventional systems.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.6
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• pp.765-771
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• 1998

An artificial neural network(NN) technique is developed for hardware redundant sensor validation. Since the measurement space is a continuous space with many operating regions, it is difficult to train a NN to correctly detect failure in an accurate measurement system. A conventional backpropagation NN is modified to include an additional preprocessing layer that extracts classification features from scalar measurements. This feature extraction means transform the measurement space to parity space. The NN is independent of the state variable being measured, the instrument range, and the signal tolerance. This NN resembles the parity space approach to signal validation, except that analytical parity equations are unneeded and the NN pattern recognition capability is utilized for decision making.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.572-574
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• 1998

This paper describes design method of control system with a pre-compensator using a neural network to compensate a error signal between a reference' signal and system response. The neural network which is used here is the mixed structure and it's algorithm is a back propagation that modify coupling coefficients. Applying this method to the position control system using DC servo motor as a driver, we verify the usefulness of this method with simulation.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.397-399
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• 1994

In this paper a feature-based automatic neural network signal classifier is presented, where five neural network algorithms such as MLP, RBF, LVQ2, MLP-Tree and LVQ-Tree are combined in parallel to classifiy various signals from their features, based on the majority vote method. To demonstrate the performance and applicability of the proposed signal classifier, some test results for the classification of synthetic waveforms and power disturbances are provided.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.10
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• pp.149-156
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• 1995

This paper presents a method of detecting chatter vibration in end-mill process. The detecting system consists of an adaptive signal processing scheme which uses an autore- gressive time-series model and a neural network is proposed and is verified its effectiveness by using acceleration and cutting force signals recorded during slotting in end-mill operations. Expeerimental results indicate that the proposed system provides excellent detection when chatter is occured within the ranges of cutting conditions considered in this study and an effectiveness of the integration of signals is confirmed.