• 전자공학회논문지SC
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• 제38권5호
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• pp.14-22
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• 2001

본 논문에서는 비선형 다변수 시스템의 신경망 식별과 신경망제어기 설계방법을 제안한다. 신경망제어기는 독립된 여러 개의 선형제어기와 하나의 신경회로망으로 구서오디며, 신경회로망은 간접 제어방식에 의해 학습된다. 제안한 제어방식을 IBM 컴퓨터 상에 구현하고 물체를 공유한 막대부하 시스템의 속도제어에 적용한다. 신경회로망의 식별능력과 제안한 제어기의 성능을 실험결과로서 살펴보고 기존의 선형제어기와 비교함으로서 제안한 제어기의 우수함을 확인한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.29.1-29
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• 2001

Neuro-fuzzy systems are multi-layered connectionist networks that realize the elements and functions of traditional fuzzy logic control/decision systems. A trained neuro-fuzzy system is isomorphic to a fuzzy logic system, and fuzzy IF-THEN rule knowledge can be explicitly extracted from the network. This talk presents a brief introduction to self-adaptive neuro-fuzzy systems and addresses some recent research results and applications. Most of the existing neuro-fuzzy systems exhibit several major drawbacks that lead to performance degradation. These drawbacks are the curse of dimensionality (i.e., fuzzy rule explosion), inability to re-structure their internal nodes in a changing environment, and their lack of ability to extract knowledge from a given set of training data. This talk focuses on our investigation of network architectures, self-adaptation algorithms, and efficient learning algorithms that will enable existing neuro-fuzzy systems to self-adapt themselves in an unstructured and uncertain environment.

• Transactions on Control, Automation and Systems Engineering
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• 제4권1호
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• pp.49-55
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• 2002

A decrease in the birthrate and aging are progressing in Japan and several countries. In that society, it is important that physically weak persons such as elderly persons are able to take care of themselves. We have been developing exoskeletal robots for human (especially for physically weak persons) motion support. In this study, the controller controls the angular position and impedance of the exoskeltal robot system using multiple fuzzy-neuro controllers based on biological signals that reflect the human subject's intention. Skin surface electromyogram (EMG) signals and the generated wrist force by the human subject during the elbow motion have been used as input information of the controller. Since the activation level of working muscles tends to vary in accordance with the flexion angle of elbow, multiple fuzzy-neuro controllers are applied in the proposed method. The multiple fuzzy-neuro controllers are moderately switched in accordance with the elbow flexion angle. Because of the adaptation ability of the fuzzy-neuro controllers, the exoskeletal robot is flexible enough to deal with biological signal such as EMG. The experimental results show the effectiveness of the proposed controller.

• Journal of Advanced Marine Engineering and Technology
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• 제20권4호
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.228-228
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• 2000

To deal with non-linearities and time-varying characteristics of hydraulic systems, in this paper, the neuro-fuzzy controller has been introduced. This controller does not require an accurate mathematical model for the nonlinear factor. In order to solve general fuzzy inference problems, the input membership function and fuzzy reasoning rules are used for determining the controller Parameters. These parameters are determined by using the learning algorithm. The control performance of the neuro-fuzzy controller is obtained through a series of experiments for the various types of input while applying disturbances to the cylinder. .and performance of this controller was compared with that of PID, PD controller. As a experimental result, it can be proven that the position tracking performance of the neuro-fuzzy is better than that of PID and PD controller.

• Journal of Power Electronics
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• 제5권1호
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• pp.1-10
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• 2005

This paper presents the application of a simple neuro-based speed control scheme of a permanent magnet (PM) dc motor. To validate its efficiency, the performance characteristics of the proposed simple neuro-based scheme are compared with those of a Neural Network controller and those of a Fuzzy Logic controller under different operating conditions. The comparative results show that the simple neuro-based speed control scheme is robust, accurate and insensitive to load disturbances.

• 한국해양생명과학회지
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• 제9권1호
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• pp.9-21
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• 2024

마비성 패류 독소(Paralytic shellfish poisoning, PSP)는 유해 조류에 의해 생성되며, 독소에 노출된 수산물을 섭취하였을 때 중독이 발생한다. 수산물 중 PSP를 검출하는 표준 시험법인 Mouse bioassay (MBA)는 낮은 검출한계와 동물 윤리 문제로 대체 시험법의 개발 필요성이 대두되고 있다. 이러한 대체 시험법 중, PSP가 신경 세포막의 Na⁺ 채널을 차단하는 기전을 이용한 마우스 뇌신경 모세포종 세포 기반 시험법(Neuro-2a assay)의 표준화를 위한 노력이 대두되고 있다. Neuro-2a assay의 원리는 Neuro-2a 세포주에 Na⁺/K⁺ ATPase 억제제인 Ouabain(O)과 Na⁺ 채널 활성화제인 Veratridine (V)을 처리하여 과도한 Na⁺ 유입으로 인한 세포사멸을 유도한 상태에서, Na⁺ 채널 억제제인 PSP를 처리하게 되면 Na⁺ 유입이 차단되어 세포가 생존하는 것을 측정하는 것이다. 본 연구에서는 PSP 검출을 위한 Neuro-2a assay를 국내 연구 환경에 맞게 다양한 매개변수를 개선하여 최적 시험법을 확립하고자 하였다. 고려한 매개변수들은 세포밀도, 배양 조건 및 PSP 처리 조건 등으로, 그 결과는 아래와 같다. 초기 세포밀도는 40,000 cells/well로, 세포 배양시간 및 처리시간은 각각 24시간으로 설정하였다. 또한 최적 O/V 농도는 500/50 μM로 설정하였다. 본 연구에서 PSP 중 Saxitoxin (STX)에 대해서 O/V 처리가 된 상태에서 S자형 용량-반응 그래프가 도출되는 8가지 농도(368~47,056 fg/μl)를 확인하였고, Neuro-2a assay의 실험실 간 변동성 비교를 통해, 실험의 적정성 확인을 위한 5가지 Quality Control Criteria와 실험 데이터의 신뢰가능 범위(Data Criteria) 6가지를 설정하였다. 확 립된 조건으로 Neuro-2a assay를 진행한 결과 반수영향농도(EC₅₀) 값은 약 1,800~3,500 fg/μl로 나타났다. 실험실 간 변동성 비교 결과, Quality Control Criteria 값 및 Data criteria 값의 변동계수(coefficients of variation (CVs))가 1.98~29.15% 범위로 산출되어 실험의 적정성 및 재현성이 확인되었다. 본 연구를 통해 우리나라에서 활용할 수 있는 PSP 검출용 Neuro-2a assay 시험법의 최적 조건 및 5가지 Quality control 기준을 제시하였고, PSP 중 대표적인 독소인 STX을 대상으로 Neuro-2a assay를 실시한 결과 유의한 EC₅₀ 값을 산출할 수 있었으며, 향후 국내 수산물을 대상으로 MBA를 대체할 수 있는 PSP 검출법으로 활용될 것으로 기대된다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.401-405
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• 1993

This paper presents a neuro adaptive control method for nonlinear dynamical systems based on artificial neural network systems. The proposed neuro adaptive controller consists of 3 layers artificial neural network system and parallel PD controller. At the early stage in learning or identification process of the system characteristics the PD controller works mainly in order to compensate for the inadequacy of the learning process and then gradually the neuro contrller begins to work instead of the PD controller after the learning process has proceeded. From the simulation studies the neuro adaptive controller is seen to be robust and works effectively for nonlinear dynamical systems from a practical applicational points of view.

• Journal of Advanced Marine Engineering and Technology
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• 제24권2호
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• pp.72-81
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• 2000

In this paper, we design an adaptive neuro-fuzzy inference system(ANFIS) precompensator for load frequency control of 2-area power systems. While proportional integral derivative (PID) controllers are used in power systems, they may have some problems because of high nonlinearities of the power systems. So, a neuro-fuzzy-based precompensation scheme is incorporated with a convectional PID controller to obtain robustness to the nonlinearities. The proposed precompensation technique can be easily implemented by adding a precompensator to an existing PID controller. The applied neruo-fuzzy inference system precompensator uses a hybrid learning algorithm. This algorithm is to use both a gradient descent method to optimize the premise parameters and a least squares method to solve for the consequent parameters. Simulation results show that the proposed control technique is superior to a conventional Ziegler-Nichols PID controller in dynamic responses about load disturbances.

• 한국생산제조학회지
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• 제22권2호
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• pp.256-262
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• 2013

This paper presents a linear path control algorithm for NeuroMate robot in a skull drilling system. For the path control inverse kinematics of the robot is analyzed and a linear interpolation algorithm is presented. A geometric approach is used for solving inverse kinematic equations for the robot. Four feasible solutions are found through the approach. The approach gives geometric insights for selecting the best solution from the feasible solutions. The presented linear interpolation algorithm computes a next position considering current velocity and remaining distance to the target position. Presented algorithm is implemented and tested in a skull drilling system.