• 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.103-106
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• 1989

A method of adaptive formation of the synthetic discriminant function(SDF) both in image plane and spatial frequency plane by using the Widrow-Hoff learning rule is proposed. The proposed method uses minimum number of interconnections between neurons so it can reduce the time for learning the neural net. Also complex valued interconnection weights are introduced for the purposes of handling the phase objects or Fourier transformed spatial frequency objects which usually have complex values for the representation of not only amplitude but also phase information. Also methods of optical implementation for the complex valued interconnection weights are discussed.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.162-164
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• 2004

This paper proposes a new classification system combining the adaptive feature weighting algorithm using the genetic algorithm and the modified KNN rule. GA is employed to choose the middle value of weights and weights of features for high performance of the system. The modified KNN rule is proposed to estimate the class of test pattern using adaptive feature space. Experiments with the unconstrained handwritten digit database of Concordia University in Canada are conducted to show the performance of the proposed method.

• 대한산업공학회지
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• 제23권1호
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• pp.129-146
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• 1997

In traditional approaches to scheduling problems, a single dispatching rule was used by all machines in a system. However, since the situation of each machine generally differs from those of other machines, it is reasonable to apply a different dispatching rule to each machine responding to its given situation. In this regard, we introduce the concept of spatial adaptation and examine its effectiveness by simulation. In the spatial adaptation, each machine in a system selects an appropriate dispatching rule in order to improve productivity while it strives to be in harmony with other machines. This study proposes an adaptive procedure which produces a reliable dispatching rule for each machine beginning with the bottleneck machine. The dispatching rule is composed of several criteria of which priorities are adaptively weighted. The weights are learned for each machine through systematic simulations. The simulations are conducted according to a Taguchi experimental design in order to find appropriate sets of criteria weights in an efficient and robust way in the context of environmental variations. The proposed method was evaluated in an application to a semiconductor wafer fabrication system. The method achieved reliable performance compared to traditional dispatching rules, and the performance quickly approached the peak after learning for only a few bottleneck machines.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.145-150
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• 1997

In this paper is studied a method of fuzzy logic control based on possibly inconsistent if-then rules representing uncertain knowledge or imprecise data. In most cases of practical applications adopting fuzzy if-then rule bases, inconsistent rules have been considered as ill-defined rules and, thus, not allowed to be in the same rule base. Note, however, that, in representing uncertain knowledge by using fuzzy if-then rules, the knowledge sometimes can not be represented in literally consistent if-then rules. In this regard, when it is hard to obtain consistent rule base, we propose the weighted rule base fuzzy logic control depending on output performance using neural network and we will derive the weight update algorithm. Computer simulations show the proposed method has good performance to deal with the inconsistent rule base fuzzy logic control. And we discuss the real application problems.

• 한국광학회:학술대회논문집
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• 한국광학회 1991년도 제6회 파동 및 레이저 학술발표회 Prodeedings of 6th Conference on Waves and Lasers
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• pp.23-28
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• 1991

A single-layer neural network with 4$\times$4 input neurons and 4 output neurons is optically implemented. Holographic lenslet arrays are used for the e optical interconnection topology, a liquid crystal light valve(LCLV) is used for controlling optical interconection weights. Using a Perceptron learning rule, it classifics input patterns into 4 different categories. It is shown that the performance of the adaptive neural network depends on the learning rate, the correlation of input patterns, and the nonlinear characteristic properties of the liquid crystal light valve.

• Structural Engineering and Mechanics
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• 제83권4호
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• pp.537-549
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• 2022

In this paper, a novel multi-feature model predictive control (MPC) framework with real-time and adaptive performances is proposed for intelligent structural control in which some drawbacks of the algorithm including, complex control rule and non-optimality, are alleviated. Hence, Linear Programming (LP) is utilized to simplify the resulted control rule. Afterward, the Whale Optimization Algorithm (WOA) is applied to the optimal and adaptive tuning of the LP weights independently at each time step. The stochastic control rule is also achieved using Kalman Filter (KF) to handle noisy measurements. The Extreme Learning Machine (ELM) is then adopted to develop a data-driven and real-time control algorithm. The efficiency of the developed algorithm is then demonstrated by numerical simulation of a twenty-story high-rise benchmark building subjected to earthquake excitations. The competency of the proposed method is proven from the aspects of optimality, stochasticity, and adaptivity compared to the KF-based MPC (KMPC) and constrained MPC (CMPC) algorithms in vibration suppression of building structures. The average value for performance indices in the near-field and far-field (El earthquakes demonstrates a reduction up to 38.3% and 32.5% compared with KMPC and CMPC, respectively.

• 전기학회논문지
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• 제60권2호
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• pp.293-300
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• 2011

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance and adaptive fuzzy learning contrroller(AFLC) for speed control in IPMSM Drives. AFLC is chaged fuzzy rule base by rule base modifier for robust control of IPMSM. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator and AFLC is confirmed by comparing to conventional algorithm.

• 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.

• 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.

• 전자공학회논문지SC
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• 제41권3호
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• pp.39-46
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• 2004

본 논문은 IPMSM 드라이브의 고성능 속도 제어를 위하여 퍼지제어와 신경회로망을 혼합 구성한 적응 FNN 제어기를 제시한다. 적응 FNN 제어기는 기준 모델에 기초한 적응 메카니즘을 적용하여 신경회로망의 고도의 적응제어와 퍼지제어기의 강인성 제어의 장점들을 접목한다. 적응 FNN 제어기의 출력은 FNN 제어기의 출력과 적응 퍼지제어의 출력을 합하여 출력을 얻는다. 적응 FNN 제어기는 다양한 동작조건에서 응답특성을 분석하고 평가한다. 제시한 적응 FNN 제어기의 타당성은 IPMSM 드라이브 시스템에 적용하여 성능 결과로 입증한다.