• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.411-415
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• 1994

An adaptive learning control scheme by use of multilayer neural networks for compensating for uncertainties in nonlinear dynamic system is examined. Multilayer neural networks are introduced to map the uncertainties in nonlinear dynamics and perform nonlinear state feedback. Parameters of neural networks are adjusted by conventional back-propagation algorithms modified with the projection operation. Effectiveness of the proposed scheme for tracking control are demonstrated through computer simulations.

• Structural Engineering and Mechanics
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• 제45권6호
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• pp.779-802
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• 2013

In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) techniques are developed and applied to identify damage in a model steel girder bridge using dynamic parameters. The required data in the form of natural frequencies are obtained from experimental modal analysis. A comparative study is made using the ANNs and ANFIS techniques and results showed that both ANFIS and ANN present good predictions. However the proposed ANFIS architecture using hybrid learning algorithm was found to perform better than the multilayer feedforward ANN which learns using the backpropagation algorithm. This paper also highlights the concept of ANNs and ANFIS followed by the detail presentation of the experimental modal analysis for natural frequencies extraction.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.446-449
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• 1997

We propose a control law in discrete time domain of the bilateral feedback teleoperation system using neural network and the reference model type of adaptive control. Different from traditional teleoperation systems, the transmission time delay irregularly changes. The proposed control method controls master and slave systems through identification of master and slave models using neural networks.

• 제어로봇시스템학회논문지
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• 제19권11호
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• pp.998-1003
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• 2013

This study proposes an adaptive control algorithm for lateral motion of a UGV (Unmanned Ground Vehicle) using an NN (Neural Networks). The lateral motion of the UGV can be corrupted with various uncertainties such as side slip. In order to compensate the performance degradation of the UGV under various uncertainties, an NN-based adaptive control is designed by utilizing a virtual control concept. Since both the drift and input gain terms are uncertain, the proposed method adapts the whole terms related to the difference between the nominal and real systems. To avoid a singularity problem with the adaptive control, the affine property of the UGV dynamic model is utilized and the overall closed-loop stability is analyzed rigorously. Finally, numerical simulations using Carsim are performed to validate the effectiveness of the proposed scheme.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 A
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• pp.187-190
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• 2003

In this paper, we address the robust adaptive backstepping controller using fuzzy neural network (FHIN) for a class of uncertain output feedback nonlinear systems with disturbance. A new algorithm is proposed for estimation of unknown bounds and adaptive control of the uncertain nonlinear systems. The state estimation is solved using K-fillers. All unknown nonlinear functions are approximated by FNN. The FNN weight adaptation rule is derived from Lyapunov stability analysis and guarantees that the adapted weight error and tracking error are bounded. The compensated controller is designed to compensate the FNN approximation error and external disturbance. Finally, simulation results show that the proposed controller can achieve favorable tracking performance and robustness with regard to unknown function and external disturbance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.931-934
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• 2005

This paper describes an adaptive recommendation system that provides real-time personalized trading advice to the investors based on their profiles and trading information environment. A proposed system integrates Stochastic technical analysis and artificial neural network that incorporates an adaptive user modeling. The user model is constructed and updated based on initial user profile and recorded user interactions with the system. The information presented to each individual user is also tailor-made to fit the user's behavior and preference. A system prototype was implemented in JAVA. Experiments used to evaluate the system's performance were done on both human subjects and synthetic users. The results show our proposed system is able to rapidly learn to provide appropriate advice to different types of users.

• 전기전자학회논문지
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• 제2권1호
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• pp.134-138
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• 1998

트래픽의 통계적 변동과 고도의 시변 특성 때문에, 최소의 반응시간을 가지고 고도의 동적인 기술과 적응 그리고 학습능력을 요구하는 네트워크의 자원으로 관리하도록 한다. 뉴럴 네트워크는 ATM 셀 도착율과 큐 길이를 정규화시키며, 그것은 적응 학습 알고리즘을 가지고, ATM 네트워크에서 발생되는 특주를 방지하기 위한 방법을 연구한다.

• 한국정보통신학회논문지
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• 제7권4호
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• pp.673-677
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• 2003

본 연구에서는 적응 필터 뱅크와 유사한 신경망을 이용한 시간영역에서의 새로운 필터뱅크(뉴럴 필터 뱅크)와 필터 창 함수를 가진 새로운 필터 뉴런을 제안하였다. 제안된 뉴럴 필터 뱅크의 성능을 검증하기 위해 두 가지의 예를 들어 실험하였다. 실험에서 제안된 기법은 기존의 방법인 주파수 영역에서의 필터뱅크보다 간단한 구조와 고속처리가 가능한 특성을 보였다. 따라서 제안된 방법은 시간 영역에서의 신호의 특징 검출에 있어 높은 성능을 제공할 것으로 사료된다.

• 한국항공우주학회지
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• 제32권4호
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• pp.24-33
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• 2004

본 논문에서는 광범위한 비선형 함수 근사 성질을 갖고 있는 온라인 적응 신경회로망을 이용하여 헬리콥터 비행 제어 시스템을 설계하였다. 기존의 시스템 모델링 오차를 보상하는 방식과는 달리, 시스템의 입출력 정보를 통해 피드백 선형화 기법에서 필요한 두 개의 비선형 함수를 신경회로망을 이용하여 대체하는 방법을 적용하였다. 두 개의 비선형 함수를 신경회로망으로 대체하여 구성된 폐회로 시스템의 추적 성능과 내부 안정성을 보장하기 위하여 신경회로망의 가중치 학습 방법을 리야프노프 함수를 이용하여 유도하였다. 그리고 헬리콥터 저속 비행 모드에 대한 수치 시뮬레이션 결과를 통해 신경회로망을 적용한 제어 시스템의 성능을 검증하였다.

• 한국광학회:학술대회논문집
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• 한국광학회 1989년도 제4회 파동 및 레이저 학술발표회 4th Conference on Waves and lasers 논문집 - 한국광학회
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• pp.85-88
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• 1989

Introducing and optimizing it-significance to the Hopfield model, ten highly correlated binary images, i.e., numbers "0" to "9", are successfully stored and retrieved in a 6x8 node system. Unlike many other neural networks models, this model has stronger error correction capability for correlated images such as "6", "8", "3", and "9". the bit-significance optimization is regarded as an adaptive learning process based on least-mean-square error algorithm, and may be implemented with another neural nets optimizer. A design for electro-optic implementation including the adaptive optimization networks is also introduced.uding the adaptive optimization networks is also introduced.