• Title/Summary/Keyword: back-propagation technique

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Modeling properties of self-compacting concrete: support vector machines approach

  • Siddique, Rafat;Aggarwal, Paratibha;Aggarwal, Yogesh;Gupta, S.M.
    • Computers and Concrete
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    • v.5 no.5
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    • pp.461-473
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    • 2008
  • The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

Robust Control of Robot Manipulator Based-on DSPs(TMS320C50) (DSPs(TMS320C50)을 이용한 로봇 매니퓰레이터의 견실제어)

  • 이우송;김종수;김홍래;한성현
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2004.10a
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    • pp.193-200
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    • 2004
  • In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

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Design of Initial Billet using the Artificial Neural Network for a Hot Forged Product (신경망을 이용한 열간단조품의 초기 소재 설계)

  • 김동진;김벙민;최재찬
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1995.04b
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    • pp.198-203
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    • 1995
  • In the paper, we have proposed a new technique to detemine the initial billet for the forged products using a function approximation in neural network. A three-layer neural network is used and a back propagation algorithm is employed totrain the network. An optimal billet which satisfied the forming limitation, minimum of incomplete filling in the die cavity, load and energyas well as more uniform distribution of effective strain, is determined by applying the ability of function approximation of te neural network. The amount of incomplete filling in the die, load and forming energyas well as effective strain are measured by the rigid-plastic finite element method. The new technique is applied tofind the optimal billet size for the axisymmetric rib-web product in hot forging. This would reduce the number of finite element simulation for determing the optimal billet of forging products, further it is usefully adapted to physical modeling for the forging design.

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Application of Neural Network Precompensated PID Controller for Load Frequency Control of Power Systems (전력계통의 부하주파수 제어를 위한 신경회로망 전 보상 PID 제어기 적용)

  • 김상효
    • Journal of Advanced Marine Engineering and Technology
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    • v.23 no.4
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    • pp.480-487
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    • 1999
  • In this paper we propose a neural network precompensated PID(NNP PID) controller for load frequency control of 2-area power system. While proportional integral derivative(PID) controllers are used in power system they have many problems because of high nonlinearities of the power system So a neural network-based precompensation scheme is adopted into a conventional PID controller to obtain a robust control to the nonlinearities. The applied neural network precompen-sator uses an error back-propagation learning algorithm having error and change of error as inputand considers the changing component of forward term of weighting factor for reducing of learning time. Simulation results show that the proposed control technique is superior to a conventional PID controller and an optimal controller in dynamic responses about load disturbances. The pro-posed technique can be easily implemented by adding a neural network precompensator to an existing PID controller.

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Experimental Studies of Neural Compensation Technique for a Fuzzy Controlled Inverted Pendulum System

  • Lee, Geun-Hyeong;Jung, Seul
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.10 no.1
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    • pp.43-48
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    • 2010
  • This article presents the experimental studies of controlling angle and position of the inverted pendulum system using neural network to compensate for errors caused due to fuzzy controller. Although fuzzy control method can deal with nonlinearities of the system, fixed fuzzy rules may not work and result in tracking errors in some cases. First, a nominal Takagi-Sugeno (TS) type fuzzy controller with fixed weights is used for controlling the inverted pendulum system. Then the neural network is added at the reference input to form the reference compensation technique (RCT)control structure. Neural network modifies the input trajectories to improve system performances by updating internal weights in on-line fashion. The back-propagation learning algorithm for neural network is derived and used to update weights. Control hardware of a DSP 6713 board to have real time control is implemented. Experimental results of controlling inverted pendulum system are conducted and performances are compared.

Speed Estimation and Control of IPMSM Drive with LM-FNN Controller (LM-FNN 제어기에 의한 IPMSM 드라이브의 속도 추정 및 제어)

  • Nam, Su-Myeong;Lee, Hong-Gyun;Ko, Jae-Sub;Choi, Jung-Sik;Chung, Dong-Hwa
    • Proceedings of the KIPE Conference
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    • 2005.07a
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    • pp.17-19
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    • 2005
  • This paper considers the design and implementation of novel technique of speed estimation and control for IPMSM using learning mechanism-fuzzy neural network(LM-FNN) and artificial neural network (ANN) control. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new hybrid Intelligent control

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Implementation of the Adaptive-Neuro Control of Robot Manipulator Using DSPs(TMS320C50) (DSPs(TMS320C50)를 이용한 로봇 매니퓰레이터의 적응-신경제어기 실현)

  • 정동연;김용태;한성현
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2002.10a
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    • pp.256-261
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    • 2002
  • In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

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An Intelligent Control of TRack Vehicle Using Fuzzy-Neural Network Control Method (퍼지-신경회로망 제어기법에 의한 궤도차량의 지능제어)

  • 신행봉;김용태;조길수;한성현
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 1999.05a
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    • pp.210-215
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    • 1999
  • In this paper, a new approach to the dynamic control technique for track vehicle system using fuzzy-neural network control technique is proposed. 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 proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-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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Optimal Hyper Analytic Wavelet Transform for Glaucoma Detection in Fundal Retinal Images

  • Raja, C.;Gangatharan, N.
    • Journal of Electrical Engineering and Technology
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    • v.10 no.4
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    • pp.1899-1909
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    • 2015
  • Glaucoma is one of the most common causes of blindness which is caused by increase of fluid pressure in the eye which damages the optic nerve and eventually causing vision loss. An automated technique to diagnose glaucoma disease can reduce the physicians’ effort in screening of Glaucoma in a person through the fundal retinal images. In this paper, optimal hyper analytic wavelet transform for Glaucoma detection technique from fundal retinal images is proposed. The optimal coefficients for transformation process are found out using the hybrid GSO-Cuckoo search algorithm. This technique consists of pre-processing module, optimal transformation module, feature extraction module and classification module. The implementation is carried out with MATLAB and the evaluation metrics employed are accuracy, sensitivity and specificity. Comparative analysis is carried out by comparing the hybrid GSO with the conventional GSO. The results reported in our paper show that the proposed technique has performed well and has achieved good evaluation metric values. Two 10- fold cross validated test runs are performed, yielding an average fitness of 91.13% and 96.2% accuracy with CGD-BPN (Conjugate Gradient Descent- Back Propagation Network) and Support Vector Machines (SVM) respectively. The techniques also gives high sensitivity and specificity values. The attained high evaluation metric values show the efficiency of detecting Glaucoma by the proposed technique.

Experimental Studies of Real- Time Decentralized Neural Network Control for an X-Y Table Robot

  • Cho, Hyun-Taek;Kim, Sung-Su;Jung, Seul
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.8 no.3
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    • pp.185-191
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    • 2008
  • In this paper, experimental studies of a neural network (NN) control technique for non-model based position control of the x-y table robot are presented. Decentralized neural networks are used to control each axis of the x-y table robot separately. For an each neural network compensator, an inverse control technique is used. The neural network control technique called the reference compensation technique (RCT) is conceptually different from the existing neural controllers in that the NN controller compensates for uncertainties in the dynamical system by modifying desired trajectories. The back-propagation learning algorithm is developed in a real time DSP board for on-line learning. Practical real time position control experiments are conducted on the x-y table robot. Experimental results of using neural networks show more excellent position tracking than that of when PD controllers are used only.