• 전자공학회논문지B
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• 제31B권8호
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• pp.211-218
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• 1994

In this paper we develope a hybrid learning rule to improve the robustness of multi-layer Perceptions. In most neural networks the activation of a neuron is deternined by a nonlinear transformation of the weighted sum of inputs to the neurons. Investigating the behaviour of activations of hidden layer neurons a new learning algorithm is developed for improved robustness for multi-layer Perceptrons. Unlike other methods which reduce the network complexity by putting restrictions on synaptic weights our method based on error-backpropagation increases the complexity of the underlying proplem by imposing it saturation requirement on hidden layer neurons. We also found that the additional gradient-descent term for the requirement corresponds to the Hebbian rule and our algorithm incorporates the Hebbian learning rule into the error back-propagation rule. Computer simulation demonstrates fast learning convergence as well as improved robustness for classification and hetero-association of patterns.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2002년도 춘계학술대회논문집
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• pp.219-227
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• 2002

In this paper, we designed neural network predictive PID controller to control sway happened in transfer of trolley for automatic travel control system. We include dynamic character of nonlinear system, and mathematical expression veny simple used neural network. When various establishment location and surrounding disturbance were approved based on mathematical modelling of crane, controller designed to become effective control location error and vibration angle of two control variables that simultaneously can predictive control. Neural network predictive PID controller produced parameter of PID controller using neural network self-tuner. Neural network self-tuner's input used crane's output and neural network predictive output. Neural network self-tuner using error back propagation algorithm. We analyzed control performance comparison through computer simulation when applied disturbance about sway of location and angle in transfer of crane. The results show that the proposed neural network predictive PID controller has better performances than general PID controller, neural network PID controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2380-2382
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• 2000

Fuzzy system which are based on membership functions and rules, can control nonlinear, uncertian, complex system well. However, Fuzzy controller has problems: It is difficult to design a stable for amateur. To update the then-part membership functions of the fuzzy controller can be designed using the error back-propagation algorithm to be minimized error. Then we could be optimized the system choosing a good performance index. The proposed fuzzy controller based on neural network is applied to control an inverted pendulum for demonstration of the robustness of proposed methodology.

• 전자공학회논문지C
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• 제34C권6호
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• pp.71-78
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• 1997

In this paper, a new neural network training algorithm will be devised for function approximator with good generalization characteristics and tested with the time series prediction problem using santaFe competition data sets. To enhance the generalization ability a constraint term of hidden neuraon activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. A hybrid learning algorithm of the error-back propagation and Hebbian learning algorithm with weight decay constraint will be naturally developed by the steepest decent algorithm minimizing the proposed cost function without much increase of computational requriements.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 1998년도 춘계종합학술대회
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• pp.96-101
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• 1998

많은 분야에서 널리 사용되고 있는 PID 제어기의 형태는 오차를 갖는 폐루프 시스템으로 구성되며, PID 제어기는 비례, 적분, 미분 제어기로 나누어진다. PID 제어기의 형태가 여러 가지로 제안되고 있지만 보다 중요한 것은 PID 제어기의 파라미터들을 어떻게 적절히 정하느냐 하는 파라미터 조정 문제이다. 실제로 산업 현장에 설치되어 있는 PID 제어기는 대부분 숙련된 기술자에 의해 수동 조작에 의한 시행 착오(trial and error) 법으로 동조되고 있다. 이 경우는 많은 노력과 시간이 소비되고, 외란(disturbance)이 첨가될 경우 적절히 동조된다는 보장도 없다. 본 논문에서는 이러한 문제를 해결하고자 신경회로망을 이용하여 PID 제어기의 파라미터를 동조하는 제어 방법을 제안하였다. 단일 뉴런으로 구성하여 구조가 간단하고, 학습에 의한 성능 개선이 가능하다. 오차 역전파(Error Back-Propagation) 알고리즘에 의하여 PID 파라미터가 되는 가중치를 자동 동조하는 방법이다. 제안한 방식의 유용성을 보이기 위해 DC 서보 모터와 비선형 시스템인 단일 관절 매니퓰레이터를 대상으로 시뮬레이션을 하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 A
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• pp.251-254
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• 1995

The Fuzzy-Neural Controller is constructed to resolve some dificulties taking place in decision of membership functions, input and output gains and an inferenced method for desinging fuzzy logic controller. In addition Neural network emulator is used to emulate induction motor forward dynamics and to get error signal at fuzzy-neural controller output layer. Error signal is backpropagated through neural network emulator. A back propagation algorithm is used to train fuzzy-neural controller and emulator. The experimental results show that this control system can provide good dynamical responses.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.43.2-43
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• 2001

In this paper, we consider the problems of the evolutionary designed neural networks structures by genetic algorithm. Neural networks has been applied to various application fields since back-propagation algorithm was proposed, e.g. function approximation, pattern or character recognition and so on. However, one of difficulties to use the neural networks. It is how to design the structure of the neural network. Researchers and users design networks structures and training parameters such as learning rate and momentum rate and so on, by trial and error based on their experiences. In the case of designing large scales neural networks, it is very hard work for manually design by try and error. For this difficulty, various structural learning algorithms have been proposed. Especially, the technique of using genetic algorithm for networks structures design has been ...

• International Journal of Contents
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• 제14권4호
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• pp.57-64
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• 2018

Among many UNESCO world heritage sites in Korea, "Historic Village: Hahoe" is adjacent to Nakdong River and it is imperative to monitor the water level near the village in a bid to forecast floods and prevent disasters resulting from floods.. In this paper, we propose a recurrent neural network with multiple hidden layers to predict the water level near the village. For training purposes on the proposed model, we adopt the sixth-order error function to improve learning for rare events as well as to prevent overspecialization to abundant events. Multiple hidden layers with recurrent and crosstalk links are helpful in acquiring the time dynamics of the relationship between rainfalls and water levels. In addition, we chose hidden nodes with linear rectifier activation functions for training on multiple hidden layers. Through simulations, we verified that the proposed model precisely predicts the water level with high peaks during the rainy season and attains better performance than the conventional multi-layer perceptron.

• 한국정밀공학회지
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• 제14권4호
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• pp.110-118
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• 1997

Multi-layer neural networks with error back-propagation algorithm have a great potential for identifying nonlinear systems with unknown characteristics. However, because they have a demerit that the speed of convergence is too slow, various methods for improving the training characteristics of backpropagition networks have been proposed. In this paper, a fuzzy division method is proposed to improve the convergence speed, which can find out an effective fuzzy division by the tuning of membership function and independently train each neural network after dividing the network model into several parts. In the simulations, the proposed method showed that the optimal fuzzy partitions could be found from the arbitray initial ones and that the convergence speed was faster than the traditional method without the fuzzy division.

• 전력전자학회논문지
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• 제11권1호
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• pp.29-37
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• 2006

본 논문에서는 LM-FNN(learning Mechanism-Fuzzy Neural Network) 제어기를 이용하여 IPMSM 드라이브의 고성능 속도를 제어한다. 고성능제어를 위하여 신경회로망과 퍼지제어를 혼합 적용한 FNN을 설계한고 더욱 성능을 개선하기 위하여 학습 메카니즘을 이용하여 FNN 제어기의 파라미터를 갱신시킨다. 그리고 ANN(Artificial Neural Network)을 이용하여 IPMSM 드라이브의 속도 추정기법을 제시한다. 추정속도의 타당성을 입증하기 위하여 시스템을 구성하여 제어특성을 분석한다. 그리고 추정된 속도를 지령속도와 비교하여 전류제어와 공간벡터 PWM을 통하여 IPMSM의 속도를 제어한다. 본 연구에서 제시한 LM-FNN과 ANN 제어기의 제어특성과 추정성능을 분석하고 그 결과를 제시한다.