• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.4
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• pp.154-157
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• 2006

This paper is proposed hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design..of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.137-140
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• 2001

In this paper, Fuzzzy Digital PI+D Controller plans for load, noise, plant change, Fuzzy Controller makes use of simple four rule and membership function, and plant used three phase Induction Motor. Characteristic of system compared from experimentation respectively the proposed Control System, Digital PID Control and Digital PI+D Control System.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.257-264
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• 2003

In the context of a dynamic trading environment, the ultimate goal of the financial forecasting system is to optimize a specific trading objective. This paper proposes a two-phase (extraction and filtering) stock trading system that aims at maximizing the rates of returns. Extraction of stocks is performed by searching specific time-series patterns described by a combination of values of technical indicators. In the filtering phase, several rules are applied to the extracted sets of stocks to select stocks to be actually traded. The filtering rules are automatically induced from past data. From a large database of daily stock prices, the values of technical indicators are calculated. They are used to make the extraction patterns, and the distributions of the discretization intervals of the values are calculated for both positive and negative data sets. We assumed that the values in the intervals of distinctive distribution may contribute to the prediction of future trend of stocks, so the rules for filtering stocks are automatically induced from the data in those intervals. We show the rates of returns when using our trading system outperform the market average. These results mean rule induction method using distributional differences is useful.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.7
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• pp.53-57
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• 2006

PID controller is widely uesd as automatic equipment for industry. However when a system has various characters of intermittence or continuance, a new parameter decision for accurate control is a hard task. As method of solving this problem, in this paper, a Neural Network gain automatic regulator as PID controller functions is presented. A property feedback control gain of system is decided by a rule of Delta learning. The function of proposed automatic Neural Network gain regulator is verified by speed control experiment results of Induction Motor.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.505-508
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• 1997

In this paper, an auto-tuning method for fuzzy controller based on the neural network is presented. The backpropagated error of neural emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and used for speed control of induction motor. For the torque control method, an indirect vector control scheme with slip calculation is used because of its stable characteristics regardless of speed. Motor input current is regulated by a current controlled voltage source PWM inverter using space voltage vector technique. Also, the scheme of current control fuzzy controller is synchronous reference frame with decoupling term. DSP(TMS320C31) is used to achieve the high speed calculation of the space voltage vector PWM and to build the self-learning fuzz. control algorithm. An IPM is used to simplify hardware design.

• Proceedings of the KIEE Conference
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• 2005.04a
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• pp.188-190
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• 2005

This paper is proposed HAI controller for high performance of induction motor drive. The design of this algorithm based on FNN controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. The control performance of the HAI controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.11a
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• pp.315-320
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• 2005

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of nor measured between the motor speed and output of a reference model. The control performance of the adaptive fuzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.7
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• pp.916-923
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• 2014

This paper proposes a low-cost switched-mode rectifier (SMR) for a small-scale wind turbine with a permanent magnet synchronous generator (PMSG) system. Also, a sensorless Fuzzy MPPT control is realized by the proposed system. In the PMSG system with the SMR, the synchronous impedance can be replaced as the input inductor of a boost converter. Moreover, the sensorless MPPT control using the Fuzzy technique is carried out by the duty-ratio regulation of the SMR. The relation between the generating power and the duty-ratio is ruled by the chain rule. The wind turbine model is implemented by the squirrel cage induction motor and generated the variable torque when the generator speed is varied. To verify the performance of the proposed system, simulation and experimental results are executed.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1195_1196
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• 2009

Light rail transit is classified to rubber-tired LRT, lear induction motor LRT, steel wheel LRT, monorail and tramway. LRT except for tramway are usually operated without driver and tramway are driven depending on driver's sight like usual car. LRT, which is one type of urban guided transit, shall be operated according to domestic "Operational Regulation for Urban Guided Transit" but existing regulation does not contain the safety related codes for driverless operation. In this paper, we investigate "IEC 62267 : Railway Application-Safety Requirements for Automated Urban Guided Transit" and compensate for existing operational regulation by referring to international standard and operational rule of commercial line.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.28 no.2
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• pp.144-150
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• 1992

This paper describes an application of artificial neural network to diagnose the defects of rotating machiner. Induction motor was used to the object of defect diagnosis. For defect diagnosis, the frequency spectrum of vibration was utilized. Learning method of applied neural network was back propagation. Neural network has following advantage; Once it has been learned, inference time is very short and it can provide a reasonable conclusion regardless of insufficient input data. So, this defect diagnosis system can be used superiorly to rule based expert system as quality inspection of rotating machinery in the shop.