• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2006년도 춘계학술대회 논문집
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• pp.309-314
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.96.2-96
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• 2001

This paper presents a control method for the experimental mobile vehicle. By merging the advantages of neural network, adaptive and fuzzy control, neural network-based adaptive fuzzy control is proposed. It can deal with a large amount of training data by neural network, from these data producing more accurate fuzzy rules by adaptive control, and then controlling the object by fuzzy control. This is not the simple combination of the three methods, but merging them into one control system Experiments and some future considerations are given.

• 조명전기설비학회논문지
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• 제21권5호
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• pp.32-43
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• 2007

IPMSM은 하중에 비하여 고출력으로 인하여 전기자동차에 널리 보급되고 있다. 본 논문은 적응 학습 퍼지-신경회로망과 ANN을 이용한 IPMSM드라이브의 최대토크 제어를 제시한다. 이러한 제어 방법은 인버터의 정격전류 및 전압값의 범위를 고려한 전속도 영역에 적용 된다. 본 논문은 적응학습 퍼지-신경회로망을 이용하여 IPMSM의 속도제어와 ANN을 이용하여 속도를 추정을 제시한다. 신경회로망의 역전파 알고리즘은 전동기 속도의 실시간 추정을 제시하는데 사용된다. 제시된 제어 알고리즘은 적응학습 퍼지-신경회로망과 ANN 제어기를 IPMSM 드라이브에 적용된다. 최대토크에 의해 제어된 동작 특성은 세부적으로 실험한다. 또한 본 논문은 적응 학습 퍼지 신경회로망과 ANN의 효과를 결과 분석을 통해 제시한다.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제53권9호
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1478-1481
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• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.247-250
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• 2001

In this paper, we propose an growing and pruning algorithm to design the adaptive structure of modular wavelet neural network(MWNN) with F-projection and geometric growing criterion. Geometric growing criterion consists of estimated error criterion considering local error and angle criterion which attempts to assign wavelet function that is nearly orthogonal to all other existing wavelet functions. These criteria provide a methodology that a network designer can constructs wavelet neural network according to one's intention. The proposed growing algorithm grows the module and the size of modules. Also, the pruning algorithm eliminates unnecessary node of module or module from constructed MWNN to overcome the problem due to localized characteristic of wavelet neural network which is used to modules of MWNN. We apply the proposed constructing algorithm of the adaptive structure of MWNN to approximation problems of 1-D function and 2-D function, and evaluate the effectiveness of the proposed algorithm.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권2호
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• pp.69-77
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• 2002

Speaker verification can be classified in two categories, text-dependent speaker verification and text-independent speaker verification. In this paper, we discuss text-dependent speaker verification. Text-dependent speaker verification system determines whether the sound characteristics of the speaker are equal to those of the specific person or not. In this paper we obtain the speaker data using a sound card in various noisy conditions, apply a new Hidden LMS (Least Mean Square) adaptive algorithm to it, and extract LPC (Linear Predictive Coding)-cepstrum coefficients as feature vectors. Finally, we use a competitive learning neural network for speaker verification. The proposed hidden LMS adaptive filter using a neural network reduces noise and enhances features in various noisy conditions. We construct a separate neural network for each speaker, which makes it unnecessary to train the whole network for a new added speaker and makes the system expansion easy. We experimentally prove that the proposed method improves the speaker verification performance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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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.

• 전력전자학회논문지
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• 제3권4호
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• pp.382-387
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• 1998

본 논문은 신경회로망을 사용하여 PWM인버터의 스위칭주파수를 일정하게 유지시키기 위한 적응 히스테리시스 밴드 전류제어 방식을 제안하였다. 신경회로망의 지도신호로 중성점전압을 고려한 적응 히스테리시스 밴드 식을 유도하고, 이 전류제어에 적합한 신경회로망의 구조 및 학습 알고리즘을 제시하였다. 시뮬레이션을 통하여 전동기의 동작 상태에 관계없이 스위칭 주파수를 거의 일정하게 유지되며, 전류의 과도응답 특성의 우수함을 확인하였다.

• 한국전자통신학회논문지
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• 제5권2호
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• pp.145-151
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• 2010

본 논문에서는 두 개의 음으로부터 전달되어온 음장의 상태를 구하여 역 필터를 구성하는 적응필터 및 신경회로망을 사용한 음장의 역 필터링 시스템을 제안한다. 본 논문에서는 최소 2승 평균법을 사용하여 FIR 필터의 계수를 계산하여 이를 갱신함으로써 역 필터링을 구축하는 방법을 사용한다. 본 논문에서 제안한 신경회로망 및 적응필터의 기법에 의하여 비선형 왜곡이 있는 간단한 파형이 학습 가능한 것을 실험 결과로부터 확인할 수 있었다.