• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.17-20
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• 2000

본 논문에서는 간단한 비선형 시냅스 회로를 이용하여 온 칩 학습기능을 포함한 모듈 칩을 구현하였다. 학습 회로는 MEBP(modified error back-propagation) 학습 규칙을 적용하여 구현하였으며, 제안된 회로는 표준 CMOS 공정으로 구현되었고, MOSIS AMI $1.5\mu\textrm{m}$공정 HSPICE 파라메터를 이용하여 그 동작을 검증하였다. 구현된 모듈 칩은 온 칩 학습기능을 가진 확장 가능한 신경회로망 칩으로 대규모의 FNNs(feedforwad neural networks) 구현에 매우 적합하리라 예상된다.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.4
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• pp.37-43
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• 1993

As an approach to develope the intelligent control scheme, this paper will propose an adaptive neural-fuzzy control scheme. The proposed neural-fuzzy control system, which consists of the Fuzzy-Neural Controller(FNC) and Model Neural Network(MNN), has two important characteristics of adaptation and learning. The error back propagation algorithm has been adopted as a learning technique.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.1003-1008
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• 1994

This paper is about an experiment of speaker-independent automation Korean spoken words recognition using Multi-Layered Perceptron and Error Back-propagation algorithm. The words were not segmented into syllables or phonemes, and some feature components extracted from the words in equal gap were applied to the neural network. This paper tried to find out the optimum conditions through various experiment which are comparison between total and pre-classified training.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.12
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• pp.1306-1316
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• 1990

A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.9
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• pp.1040-1047
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• 2011

This paper proposes a novel method based on error back propagation neural networks to fuse laser sensor data and ultrasonic sensor data for enhancing the accuracy of mapping. For navigation of single robot, the robot has to know its initial position and accurate environment information around it. However, due to the inherent properties of sensors, each sensor has its own advantages and drawbacks. In our system, the robot equipped with seven ultrasonic sensors and a laser sensor navigates to map two different corridor environments. The experimental results show the effectiveness of the heterogeneous sensor fusion using an error backpropagation algorithm for mapping.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.1
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• pp.8-14
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• 2005

This paper is proposed hybrid artificial intelligent(HAI) controller for speed control of surface permanent magnet synchronous motor(SPMSM) drive. The design of this algorithm based on HAI 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 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.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.9
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• pp.569-575
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• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) 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. 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 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 KIEE Conference
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• 2006.10c
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• pp.597-599
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• 2006

In this paper, we propose an artificial intelligence method to compensate the nonlinearity error which occurs in the heterodyne laser interferometer. Some superior properties such as long measurement range, ultra-precise resolution and various system set-up lead the laser interferometer to be a practical displacement measurement apparatus in various industry and research area. In ultra-precise measurement such as nanometer or subnanometer scale, however, the accuracy is limited by the nonlinearity error caused by the optical parts. The feedforward neural network trained by back-propagation with a capacitive sensor as a reference signal minimizes the nonlinearity error and we demonstrate the effectiveness of our proppsed algorithm through some experimental results.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.570-572
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• 2005

This paper is proposed adaptive 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 KIEE Conference
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• 1993.11a
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• pp.3-5
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• 1993

This paper presents a identification techniques of coherent generators for dynamic equivalents using artificial neural networks. In the developed neural network, inputs are the power system parameters which have a property of coherency. Outputs of the neural network are coherency and error indices which are derived from density measure concept. The learning of developed neural network is carried out by means of error back-propagation algorithm. Identification of coherent generators are implemented by proposed grouping algorithm using coherency and error indices. The proposed method is confirmed by simulations for 39-bus New England system.