• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.3004-3006
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• 2000

In this paper, we propose an optimal design method of Fuzzy-Neural Networks model with parallel structure for complex and nonlinear systems. The proposed model is consists of a multiple number of FNN connected in parallel. The proposed FNNs with parallel structure is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. We use a HCM clustering and GAs to identify the structure and the parameters of the proposed model. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model. we use the time series data for gas furnace and the numerical data of nonlinear function.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.654-656
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• 1998

This paper presents an optimal identification method of nonlinear and complex system that is based on fuzzy-neural network(FNN). The FNN used simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM Algorithm to find initial parameters of membership function. And then to obtain optimal parameters, we use the genetic algorithm. Genetic algorithm is a random search algorithm which can find the global optimum without converging to local optimum. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance of the FNN, we use the time series data for 9as furnace and the sewage treatment process.

• Journal of Information Processing Systems
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• 제18권1호
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• pp.12-25
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• 2022

In recent years, deep convolutional neural networks have made significant progress in the research of single image super-resolution. However, it is difficult to be applied in practical computing terminals or embedded devices due to a large number of parameters and computational effort. To balance these problems, we propose CSRNet, a lightweight neural network based on channel split residual learning structure, to reconstruct highresolution images from low-resolution images. Lightweight refers to designing a neural network with fewer parameters and a simplified structure for lower memory consumption and faster inference speed. At the same time, it is ensured that the performance of recovering high-resolution images is not degraded. In CSRNet, we reduce the parameters and computation by channel split residual learning. Simultaneously, we propose a double-upsampling network structure to improve the performance of the lightweight super-resolution network and make it easy to train. Finally, we propose a new evaluation metric for the lightweight approaches named 100_FPS. Experiments show that our proposed CSRNet not only speeds up the inference of the neural network and reduces memory consumption, but also performs well on single image super-resolution.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2849-2852
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• 1999

This paper describes the fuzzy PD controller using simplified indirect inference method. First, the fuzzy PD controller is derived from the conventional continuous time linear PD controller. Then the fuzzification, control-rule base, and defuzzification using SIIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional PD controller. which has the same linear structure. but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability. particularly when the process to be controlled is nonlinear. As the SIIM is applied, the fuzzy Inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the Proposed method has the capability of the high speed inference and extending the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control Performance of the one Proposed by D. Misir et at. Final)y. we simulated the contour control of the X-Y tables with direct control strategies using the proposed fuzzy PD controller.

• 한국멀티미디어학회논문지
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• 제19권5호
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• pp.865-873
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• 2016

In recent years, Tracking-Learning-Detection(TLD) system has been widely used as a detection and tracking algorithm for vision sensors. While conventional algorithms are vulnerable to occlusion, and changes in illumination and appearances, TLD system is capable of robust tracking by conducting tracking, detection, and learning in real time. However, the detection and tracking algorithms of TLD system utilize rotation-variant features, and the margin of tracking error becomes greater when an object makes a full out-of-plane rotation. Thus, we propose a rotation-invariant TLD system(RI-TLD). we propose a simplified average orientation histogram and rotation matrix for a rotation inference algorithm. Experimental results with various tracking tests demonstrate the robustness and efficiency of the proposed system.

• 대한전기학회논문지
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• 제43권4호
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• pp.627-637
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• 1994

In this study, a predictive fuzzy control algorithm to supervise the elevator system with plural cars is developed and its performance is evaluated. The proposed algorithm is based on fuzzy in-ference system to cope with multiple control objects and uncertainty of system state. The control objects are represented as linguistic predictive fuzzy rules and simplified reasoning method is utilized as a fuzzy inference method. Real-time simulation is performed with respect o all possible modes of control, and the resultant controls ard predicted. The predicted rusults are then utilized as the control in-puts of the fuzzy rules. The feasibility of the proposed control algorithm is evaluated by graphic simulator on computer. Finallu, the results of graphic simulation is compared with those of a conventional group control algorighm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.157.6-157
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• 2001

We estimated various neuron functions to construct of engineering neurons, which are the combination of sigmoid, linear, sine, quadric, double/single bended, soft max/minimum functions. These combinations are estimated by the property on the potential surface between the learning points, calculation speed, and learning convergence; because the surface depends on the inference ability of a neuron system; and speed and convergence are depend on the efficiency on the points of engineering applications. After the evaluating discussions, we can select more appropriate combination than original sigmoid function´s, which is single bended function and linear one. The combination ...

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.68-68
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• 2000

This paper presents a fuzzy modified PID controller that uses linear fuzzy inference method. In this structure, the proportional and derivative gains vary with the output of the system under control. 2-input PD type fuzzy controller is designed to obtain the varying gains. The proposed fuzzy PID structure maintains the same performance as the general-purpose linear PID controller, and enhances the tracking performance over a wide range of input. Numerical simulations and experimental results show the effectiveness of the fuzzy PID controller in comparison with the conventional PID controller.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.929-932
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• 1993

A fuzzy microcontroller is presented implementing a simplified inference mechanism. Fuzzification, rule composition and defuzzification are carried out by means of (basically) analog current-mode CMOS circuits operating in strong inversion. Also a voltage interface is provided with the external world. Combining analog and digital techniques allow a programming capability.

• 한국산학기술학회논문지
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• 제9권4호
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• pp.911-917
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• 2008

본 논문에서는 지능형 디밍 컨버터의 새로운 구조 및 설계 방법론을 소개하고 일련의 수치적인 실험을 통하여 제안된 모델 및 시스템을 평가한다. 형광 램프용 디밍 전자식 안정기는 전용의 디밍 IC를 사용하여 기존의 전자식 안정기 대비 최대 83%의 램프 수명 및 안정기 수명 연장을 가능하게 했다. 하지만 이러한 장점은 사용자가 디밍 컨트롤 스위치를 통하여 수동으로 제어를 해야 하는 불편함 뿐만 아니라, 수동 제어가 불가능 할 경우 에너지 절약과 램프의 수명 연장의 실효를 얻을 수 없다. 따라서 본 논문에서는 지능형 퍼지 이론(Fuzzy Inference System)을 전자식 안정기에 접목시켜 지능형 디밍 컨버터 기반 전자식 안정기에 대한 연구 및 외부조도 조건과 사용자 설정에 따른 에너지 절약을 도모하는데 중점을 두었다. 마지막으로 제안된 시스템의 하드웨어에 지능형 모델을 적용함으로써 기존 전자식 안정기 대비 성능평가를 통해 지능형 디밍 컨버터의 우수성을 보인다.