• Title/Summary/Keyword: neural network.

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Experimental Studies of neural Network Control Technique for Nonlinear Systems (신경회로망을 이용한 비선형 시스템 제어의 실험적 연구)

  • Jeong, Seul;Yim, Sun-Bin
    • Journal of Institute of Control, Robotics and Systems
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    • v.7 no.11
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    • pp.918-926
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    • 2001
  • In this paper, intelligent control method using neural network as a nonlinear controller is presented. Simulation studies for three link rotary robot are performed. Neural network controller is implemented on DSP board in PC to make real time computing possible. On-line training algorithms for neural network control are proposed. As a test-bed, a large x-y table was build and interface with PC has been implemented. Experiments such as inverted pendulum control and large x-y table position control are performed. The results for different PD controller gains with neural network show excellent position tracking for circular trajectory compared with those for PD controller only. Neural control scheme also works better for controlling inverted pendulum on x-y table.

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Determination of Initial Billet using The Artificial Neural Networks and The Finite Element Method for The Forged Products (신경망과 유한요소법을 이용한 단조품의 초기 소재 결정)

  • 김동진;고대철;김병민;강범수;최재찬
    • Proceedings of the Korean Society for Technology of Plasticity Conference
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    • 1994.10a
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    • pp.133-140
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    • 1994
  • In this paper, we have proposed a new method to determine the initial billet for the forged products using a function approximation in neural networks. the architecture of neural network is a three-layer neural network and the back propagation algorithm is employed to train the network. By utilizing the ability of function approximation of neural network, an optimal billet is determined by applying nonlinear mathematical relationship between shape ratio in the initial billet and the final products. A volume of incomplete filling in the die is measured by the rigid-plastic finite element method. The neural network is trained with the initial billet shape ratio and that of the un-filled volume. After learning, the system is able to predict the filling region which are exactly the same or slightly different to results of finite element method. It is found that the prediction of the filling shape ratio region can be made successfully and the finite element method results are represented better by the neural network.

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Training an Artificial Neural Network for Estimating the Power Flow State

  • Sedaghati, Alireza
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.275-280
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    • 2005
  • The principal context of this research is the approach to an artificial neural network algorithm which solves multivariable nonlinear equation systems by estimating the state of line power flow. First a dynamical neural network with feedback is used to find the minimum value of the objective function at each iteration of the state estimator algorithm. In second step a two-layer neural network structures is derived to implement all of the different matrix-vector products that arise in neural network state estimator analysis. For hardware requirements, as they relate to the total number of internal connections, the architecture developed here preserves in its structure the pronounced sparsity of power networks for which state the estimator analysis is to be carried out. A principal feature of the architecture is that the computing time overheads in solution are independent of the dimensions or structure of the equation system. It is here where the ultrahigh-speed of massively parallel computing in neural networks can offer major practical benefit.

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The combined feedforward/fedback controller design using jacobians of neural network (신경회로망의 쟈쿄비안을 이용한 feedforward/feedback 병합제어기 설계)

  • 조규상;임제택
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.33B no.2
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    • pp.140-148
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    • 1996
  • This paper proposes a combined feedforward/feedback controller which uses jacobians of neural network. The jacobians are calculated form the neural network that identifies the nonlinear plant, which are used for designing a jacobian controller and for training a neural network controller. Normally, it takes much time to train the neural network controller. Combining the neural and the jacobian controller, it can be a stable controller from the beginning of training phase of neural network, and it can be implemented as a learning-while-functioning controller. Simulated resutls for the proposed controller show its effectiveness and better performances.

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Dynamic Control of Track Vehicle Using Fuzzy-Neural Control Method (퍼지-뉴럴 제어기법에 의한 궤도차량의 동적 제어)

  • 한성현;서운학;조길수;윤강섭
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1997.04a
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    • pp.133-139
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    • 1997
  • This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is propored a learning controller consisting of two neural network-fuzzy based on independent resoning and a connection net with fixed weights to simply the neural network-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle

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A MNN(Modular Neural Network) for Robot Endeffector Recognition (로봇 Endeffector 인식을 위한 모듈라 신경회로망)

  • 김영부;박동선
    • Proceedings of the IEEK Conference
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    • 1999.06a
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    • pp.496-499
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    • 1999
  • This paper describes a medular neural network(MNN) for a vision system which tracks a given object using a sequence of images from a camera unit. The MNN is used to precisely recognize the given robot endeffector and to minize the processing time. Since the robot endeffector can be viewed in many different shapes in 3-D space, a MNN structure, which contains a set of feedforwared neural networks, co be more attractive in recognizing the given object. Each single neural network learns the endeffector with a cluster of training patterns. The training patterns for a neural network share the similar charateristics so that they can be easily trained. The trained MNN is less sensitive to noise and it shows the better performance in recognizing the endeffector. The recognition rate of MNN is enhanced by 14% over the single neural network. A vision system with the MNN can precisely recognize the endeffector and place it at the center of a display for a remote operator.

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A Comparative Study on the Prediction of KOSPI 200 Using Intelligent Approaches

  • Bae, Hyeon;Kim, Sung-Shin;Kim, Hae-Gyun;Woo, Kwang-Bang
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.3 no.1
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    • pp.7-12
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    • 2003
  • In recent years, many attempts have been made to predict the behavior of bonds, currencies, stock or other economic markets. Most previous experiments used the neural network models for the stock market forecasting. The KOSPI 200 (Korea Composite Stock Price Index 200) is modeled by using different neural networks and fuzzy logic. In this paper, the neural network, the dynamic polynomial neural network (DPNN) and the fuzzy logic employed for the prediction of the KOSPI 200. The prediction results are compared by the root mean squared error (RMSE) and scatter plot, respectively. The results show that the performance of the fuzzy system is little bit worse than that of the DPNN but better than that of the neural network. We can develop the desired fuzzy system by optimization methods.

Neural Network Model Compression Algorithms for Image Classification in Embedded Systems (임베디드 시스템에서의 객체 분류를 위한 인공 신경망 경량화 연구)

  • Shin, Heejung;Oh, Hyondong
    • The Journal of Korea Robotics Society
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    • v.17 no.2
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    • pp.133-141
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    • 2022
  • This paper introduces model compression algorithms which make a deep neural network smaller and faster for embedded systems. The model compression algorithms can be largely categorized into pruning, quantization and knowledge distillation. In this study, gradual pruning, quantization aware training, and knowledge distillation which learns the activation boundary in the hidden layer of the teacher neural network are integrated. As a large deep neural network is compressed and accelerated by these algorithms, embedded computing boards can run the deep neural network much faster with less memory usage while preserving the reasonable accuracy. To evaluate the performance of the compressed neural networks, we evaluate the size, latency and accuracy of the deep neural network, DenseNet201, for image classification with CIFAR-10 dataset on the NVIDIA Jetson Xavier.

Speech and Noise Recognition System by Neural Network (신경회로망에 의한 음성 및 잡음 인식 시스템)

  • Choi, Jae-Sung
    • The Journal of the Korea institute of electronic communication sciences
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    • v.5 no.4
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    • pp.357-362
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    • 2010
  • This paper proposes the speech and noise recognition system by using a neural network in order to detect the speech and noise sections at each frame. The proposed neural network consists of a layered neural network training by back-propagation algorithm. First, a power spectrum obtained by fast Fourier transform and linear predictive coefficients are used as the input to the neural network for each frame, then the neural network is trained using these power spectrum and linear predictive coefficients. Therefore, the proposed neural network can train using clean speech and noise. The performance of the proposed recognition system was evaluated based on the recognition rate using various speeches and white, printer, road, and car noises. In this experiment, the recognition rates were 92% or more for such speech and noise when training data and evaluation data were the different.

Implementation of Self-Adaptative System using Algorithm of Neural Network Learning Gain (신경회로망 학습이득 알고리즘을 이용한 자율적응 시스템 구현)

  • Lee, Sung-Su
    • Proceedings of the KIEE Conference
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    • 2006.07d
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    • pp.1868-1870
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    • 2006
  • Neural network is used in many fields of control systems, but input-output patterns of a control system are not easy to be obtained and by using as single feedback neural network controller. And also it is difficult to get a satisfied performance when the changes of rapid load and disturbance are applied. To resolve those problems, this paper proposes a new algorithm which is the neural network controller. The new algorithm uses the neural network instead of activation function to control object at the output node. Therefore, control object is composed of neural network controller unifying activation function, and it supplies the error back propagation path to calculate the error at the output node. As a result, the input-output pattern problem of the controller which is resigned by the simple structure of neural network is solved, and real-time learning can be possible in general back propagation algorithm. Application of the new algorithm of neural network controller gives excellent performance for initial and tracking response and it shows the robust performance for rapid load change and disturbance. The proposed control algorithm is implemented on a high speed DSP, TMS320C32, for the speed of 3-phase induction motor. Enhanced performance is shown in the test of the speed control.

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