• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.566-568
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• 1998

In this paper, we suggest a optimal design method of Fuzzy-Neural Networks model for complex and nonlinear systems. FNNs have the stucture of fusion of both fuzzy inference with linguistic variables and Neural Networks. The network structure uses the simpified inference as fuzzy inference system and the BP algorithm as learning procedure. And we use a clustering algorithm to find initial parameters of membership function. 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 index, we use the time series data for gas furnace and the sewage treatment process.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집B
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• pp.105-109
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• 2001

This paper discuss the real-time self-tuning learning control based on evolutionary computation, which proves its the superiority in the finding of the optimal solution at the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.577-583
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• 1994

The purpose of this paper is to study the force control law which can be implemented on a non-modified robot system. The external force control algorithm proposed in this paper can be designed by means of a classical and modern control law. We showed the validation and the possibility of muti-dimensional force control idea through the simulation and experiments. Also, the Iterative learning control is studied for compensating errors due to thr disturbances and nonlinear effects. The previous information(control input, error) was used to determine the control input of next trial. The experimental result show the vaidity of this algorithm.

• 한국지능시스템학회논문지
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• 제7권3호
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• pp.3-11
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• 1997

비선형 동적 시스템을 제어하기에 적합한 귀한 신경망에 대한 주요한 연구중의 하나는 안정적인 학습 알고리듬을 개발하는 것이다. 본 논문에서는 진화 연산 알고리듬을 이용한 안정작인 귀환 신경망의 학습 알고리듬을 개발한다. 또한 개발한 학습 알고리듬을 사용한 귀환 신경망을 전형적인 비선형 동적 시스템인 로보트 매니퓰레이터의 제어 시스템에 적용하고 개발한 제어 알고리듬의 효용성을 입증한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.186-189
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• 1990

The automization of manufacturing lines may be accomplished by replacing the human operator with computer system. This paper describes an idea to fully automize the razor qrinding process. Now, in this system, to control the process, human operator must estimate the qrinded states and control the grinding machine continuously. We propose two methods to automize this process by using CMAC memory. One is about learning expert-rules without direct communication with operator. And the other is complete self-learning method based on CMAC's learning algorithm. These ideas may be applied for another manufacturing processes.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1017-1020
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• 1993

This paper describes a neo fuzzy neuron which was produced by a fusion of fuzzy logic and neuroscience. Some learning algorithms are presented. The guarantee for the global minimum on the error-weight space is proved by a reduction to absurdity. Enhanced is that the learning speed of the neo fuzzy neuron exceeds 100,000 times of that of conventional multi-layer neural networks.

• 센서학회지
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• 제13권3호
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• pp.224-229
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• 2004

In this paper, we propose a new Active Noise Control (ANC) system using a teacher forced Blind Source Separation (BSS) algorithm. The Blind Source Separation based on the Independent Component Analysis (ICA) separates the desired sound signal from the unwanted noise signal. In the proposed system, the BSS algorithm is used as a preprocessor of ANC system. Also, we develop a teacher forced BSS learning algorithm to enhance the performance of BSS. The teacher signal is obtained from the output signal of the ANC system. Computer experimental results show that the proposed ANC system in conjunction with the BSS algorithm effectively cancels only the ship engine noise signal from the linear and convolved mixtures with human voice.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.163-168
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• 2002

The voting algorithm for action selection performs self-improvement by Reinforcement learning algorithm in the dynamic environment. The proposed voting algorithm improves the navigation of the robot by adapting the eligibility of the behaviors and determining the Command Set Generator (CGS). The Navigator that using a proposed voting algorithm corresponds to the CGS for giving the weight values and taking the reward values. It is necessary to decide which Command Set control the mobile robot at given time and to select among the candidate actions. The Command Set was learnt online by means as Q-learning. Action Selector compares Q-values of Navigator with Heterogeneous behaviors. Finally, real-world experimentation was carried out. Results show the good performance for the selection on command set as well as the convergence of Q-value.

• 한국전자통신학회논문지
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• 제16권4호
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• pp.699-708
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• 2021

본 논문에서는 2개의 비전 센서와 딥 러닝을 이용한 자율주행 차량의 속도제어 알고리즘을 제시하였다. 비전 센서 A로부터 제공되는 도로 속도 표지판 영상에 딥 러닝 프로그램인 텐서플로우를 이용하여 속도 표지를 인식한 후, 자동차가 인식된 속도를 따르도록 하는 자동차 속도 제어 알고리즘을 제시하였다. 동시에 비전 센서 B부터 전송되는 도로 영상을 실시간으로 분석하여 차선을 검출하고 조향 각을 계산하며 PWM 제어를 통해 전륜 차축을 제어, 차량이 차선을 추적하도록 하는 조향 각 제어 알고리즘을 개발하였다. 제안된 조향 각 및 속도 제어 알고리즘의 유효성을 검증하기 위해서 파이썬 언어, 라즈베리 파이 및 Open CV를 기반으로 하는 자동차 시작품을 제작하였다. 또한, 시험 제작한 트랙에서 조향 및 속도 제어에 관한 시나리오를 검증함으로써 정확성을 확인할 수 있었다.

• 한국정밀공학회지
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• 제20권7호
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• pp.84-90
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• 2003

This paper develops a control method that is composed of the proportional control algorithm and the learning algorithm based on the recurrent neural networks (RNN) for the position control of a pneumatic rodless cylinder. The proportional control algorithm is suggested for the modeled pneumatic system, which is obtained easily simplifying the system, and the RNN is suggested for the compensation of the modeling errors and uncertainties of the pneumatic system. In the proportional control, two zones are suggested in the phase plane. One is the transient zone for the smooth tracking and the other is the small movement zone for the accurate position control with eliminating the stick-slip phenomenon. The RNN is connected in parallel with the proportional control for the compensation of modeling errors and frictions, compressibilities, and parameter uncertainties in the pneumatic control system. This paper experimentally verifies the feasibility of the proposed control algorithm for such pneumatic systems.