• Title, Summary, Keyword: Artificial neural network ANN

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The Size Reduction of Artificial Neural Network by Destroying the Connections (연결선 파괴에 의한 인공 신경망의 크기 축소)

  • 이재식;이혁주
    • Journal of the Korean Operations Research and Management Science Society
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    • v.27 no.1
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    • pp.33-51
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    • 2002
  • A fully connected Artificial Neural Network (ANN) contains many connections. Compared to the pruned ANN with fewer connections, the fully connected ANN takes longer time to produce solutions end may not provide appropriate solutions to new unseen date. Therefore, by reducing the sloe of ANN, we can overcome the overfitting problem and increase the computing speed. In this research, we reduced the size of ANN by destroying the connections. In other words, we investigated the performance change of the reduced ANN by systematically destroying the connections. Then we found the acceptable level of connection-destruction on which the resulting ANN Performs as well as the original fully connected ANN. In the previous researches on the sloe reduction of ANN, the reduced ANN had to be retrained every time some connections were eliminated. Therefore, It tool lolly time to obtain the reduced ANN. In this research, however, we provide the acceptable level of connection-destruction according to the size of the fully connected ANN. Therefore, by applying the acceptable level of connection-destruction to the fully connected ANN without any retraining, the reduced ANN can be obtained efficiently.

Development of Artificial Neural Network Model for the Prediction of Descending Time of Room Air Temperature (실온하강신간 예측을 위한 신경망 모델의 개발)

  • 양인호;김광우
    • Korean Journal of Air-Conditioning and Refrigeration Engineering
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    • v.12 no.11
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    • pp.1038-1047
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    • 2000
  • The objective of this study is to develop an optimized Artificial Neural Network(ANN) model to predict the descending time of room air temperature. For this, program for predicting room air temperature and ANN program using generalized delta rule were collected through simulation for predicting room air temperature. ANN was trained and the ANN model having the optimized values-learning rate, moment, bias, number of hidden layer, and number of neuron of hidden layer was presented.

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Two Layer Multiquadric-Biharmonic Artificial Neural Network for Area Quasigeoid Surface Approximation with GPS-Levelling Data

  • Deng, Xingsheng;Wang, Xinzhou
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • v.2
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    • pp.101-106
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    • 2006
  • The geoidal undulations are needed for determining the orthometric heights from the Global Positioning System GPS-derived ellipsoidal heights. There are several methods for geoidal undulation determination. The paper presents a method employing a simple architecture Two Layer Multiquadric-Biharmonic Artificial Neural Network (TLMB-ANN) to approximate an area of 4200 square kilometres quasigeoid surface with GPS-levelling data. Hardy’s Multiquadric-Biharmonic functions is used as the hidden layer neurons’ activation function and Levenberg-Marquardt algorithm is used to train the artificial neural network. In numerical examples five surfaces were compared: the gravimetric geometry hybrid quasigeoid, Support Vector Machine (SVM) model, Hybrid Fuzzy Neural Network (HFNN) model, Traditional Three Layer Artificial Neural Network (ANN) with tanh activation function and TLMB-ANN surface approximation. The effectiveness of TLMB-ANN surface approximation depends on the number of control points. If the number of well-distributed control points is sufficiently large, the results are similar with those obtained by gravity and geometry hybrid method. Importantly, TLMB-ANN surface approximation model possesses good extrapolation performance with high precision.

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Indirect Cutting Force Estimation Using Artificial Neural Network (인공 신경망을 이용한 절삭력 간접 측정)

  • 최지현;김종원
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.1054-1058
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    • 1995
  • There have been many research works for the indirect cutting force measurement in machining process, which deal with the case of one-axis cutting process. In multi-axis cutting process, the main difficulties to estimate the cutting forces occur when the feed direction is reversed. This paper presents the indirect cutting force measurement method in contour NC milling processes by using current signals of servo motors. An artificial neural network (ANN) system are suggested. An artificial neural network(ANN) system is also implemented with a training set of experimental cutting data to measure cutting force indirectly. The input variables of the ANN system are the motor currents and the feedrates of x and y-axis servo motors, and output variable is the cutting force of each axis. A series of experimental works on the circular interpolated contour milling process with the path of a complete circle has been performed. It is concluded that by comparing the ANN system with a dynamometer measuring cutting force directil, the ANN system has a good performance.

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River Stage Forecasting Model Combining Wavelet Packet Transform and Artificial Neural Network (웨이블릿 패킷변환과 신경망을 결합한 하천수위 예측모델)

  • Seo, Youngmin
    • Journal of Environmental Science International
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    • v.24 no.8
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    • pp.1023-1036
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    • 2015
  • A reliable streamflow forecasting is essential for flood disaster prevention, reservoir operation, water supply and water resources management. This study proposes a hybrid model for river stage forecasting and investigates its accuracy. The proposed model is the wavelet packet-based artificial neural network(WPANN). Wavelet packet transform(WPT) module in WPANN model is employed to decompose an input time series into approximation and detail components. The decomposed time series are then used as inputs of artificial neural network(ANN) module in WPANN model. Based on model performance indexes, WPANN models are found to produce better efficiency than ANN model. WPANN-sym10 model yields the best performance among all other models. It is found that WPT improves the accuracy of ANN model. The results obtained from this study indicate that the conjunction of WPT and ANN can improve the efficiency of ANN model and can be a potential tool for forecasting river stage more accurately.

The Effect of Seasonal Input on Predicting Groundwater Level Using Artificial Neural Network (인공신경망을 이용한 지하수위 예측과 계절효과 반영을 위한 입력치의 영향)

  • Kim, Incheol;Lee, Junhwan
    • Ecology and Resilient Infrastructure
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    • v.5 no.3
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    • pp.125-133
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    • 2018
  • Artificial neural network (ANN) is a powerful model to predict time series data and have been frequently adopted to predict groundwater level (GWL). Many researchers have also tried to improve the performance of ANN prediction for GWL in many ways. Dummies are usually used in ANN as input to reflect the seasonal effect on predicted results, which is necessary for improving the predicting performance of ANN. In this study, the effect of Dummy on the prediction performance was analyzed qualitatively and quantitatively using several graphical methods, correlation coefficient and performance index. It was observed that results predicted using dummies for ANN model indicated worse performance than those without dummies.

Artificial Neural Network based Motion Classification Algorithm using Surface Electromyogram (표면 근전도를 이용한 Artificial Neural Network 기반의 동작 분류 알고리즘)

  • Jeong, E.C.;Kim, S.J.;Song, Y.R.;Lee, S.M.
    • Journal of rehabilitation welfare engineering & assistive technology
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    • v.6 no.1
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    • pp.67-73
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    • 2012
  • In this paper, Artificial Neural Network(ANN) based motion classification algorithm is proposed to classify wrist motions using surface electromyograms(sEMG). surface EMGs are obtained from two electrodes placed on the flexor carpi ulnaris muscle and extensor carpi ulnaris muscle of 26 subjects under no strain condition during wrist motions and used to recognize wrist motions such as up, down, left, right, and rest. Feature is extracted from obtained EMG signals in time domain for fast processing and used to classify wrist motions using ANN. DAMV, DASDV, MAV, and RMS were used as features and accuracies of motion classification based on ANN were 98.03% for DAMV, 97.97% for DASDV, 96.95% for MAV, 96.82% for RMS.

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Maximum Torque Control of IPMSM with Adoptive Leaning Fuzzy-Neural Network (적응학습 퍼지-신경회로망에 의한 IPMSM의 최대토크 제어)

  • Chung, Dong-Hwa;Ko, Jae-Sub;Choi, Jung-Sik
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.21 no.5
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    • pp.32-43
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    • 2007
  • Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. This paper proposes speed control of IPMSM using adaptive learning fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive learning fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive learning fuzzy neural network and artificial neural network.

Multiple Network-on-Chip Model for High Performance Neural Network

  • Dong, Yiping;Li, Ce;Lin, Zhen;Watanabe, Takahiro
    • JSTS:Journal of Semiconductor Technology and Science
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    • v.10 no.1
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    • pp.28-36
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    • 2010
  • Hardware implementation methods for Artificial Neural Network (ANN) have been researched for a long time to achieve high performance. We have proposed a Network on Chip (NoC) for ANN, and this architecture can reduce communication load and increase performance when an implemented ANN is small. In this paper, a multiple NoC models are proposed for ANN, which can implement both a small size ANN and a large size one. The simulation result shows that the proposed multiple NoC models can reduce communication load, increase system performance of connection-per-second (CPS), and reduce system running time compared with the existing hardware ANN. Furthermore, this architecture is reconfigurable and reparable. It can be used to implement different applications of ANN.

A study on the prediction of injection pressure and weight of injection-molded product using Artificial Neural Network (Artificial Neural Network를 이용한 사출압력과 사출성형품의 무게 예측에 대한 연구)

  • Yang, Dong-Cheol;Kim, Jong-Sun
    • Journal of the Korea Society of Die & Mold Engineering
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    • v.13 no.3
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    • pp.53-58
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    • 2019
  • This paper presents Artificial Neural Network(ANN) method to predict maximum injection pressure of injection molding machine and weights of injection molding products. 5 hidden layers with 10 neurons is used in the ANN. The ANN was conducted with 5 Input parameters and 2 response data. The input parameters, i.e., melt temperature, mold temperature, fill time, packing pressure, and packing time were selected. The combination of the orthogonal array L27 data set and 23 randomly generated data set were applied in order to train and test for ANN. According to the experimental result, error of the ANN for weights was $0.49{\pm}0.23%$. In case of maximum injection pressure, error of the ANN was $1.40{\pm}1.19%$. This value showed that ANN can be successfully predict the injection pressure and the weights of injection molding products.

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