• Title, Summary, Keyword: 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.

ON GRAPHS ASSOCIATED WITH MODULES OVER COMMUTATIVE RINGS

  • Pirzada, Shariefuddin;Raja, Rameez
    • Journal of the Korean Mathematical Society
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    • v.53 no.5
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    • pp.1167-1182
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    • 2016
  • Let M be an R-module, where R is a commutative ring with identity 1 and let G(V,E) be a graph. In this paper, we study the graphs associated with modules over commutative rings. We associate three simple graphs $ann_f({\Gamma}(M_R))$, $ann_s({\Gamma}(M_R))$ and $ann_t({\Gamma}(M_R))$ to M called full annihilating, semi-annihilating and star-annihilating graph. When M is finite over R, we investigate metric dimensions in $ann_f({\Gamma}(M_R))$, $ann_s({\Gamma}(M_R))$ and $ann_t({\Gamma}(M_R))$. We show that M over R is finite if and only if the metric dimension of the graph $ann_f({\Gamma}(M_R))$ is finite. We further show that the graphs $ann_f({\Gamma}(M_R))$, $ann_s({\Gamma}(M_R))$ and $ann_t({\Gamma}(M_R))$ are empty if and only if M is a prime-multiplication-like R-module. We investigate the case when M is a free R-module, where R is an integral domain and show that the graphs $ann_f({\Gamma}(M_R))$, $ann_s({\Gamma}(M_R))$ and $ann_t({\Gamma}(M_R))$ are empty if and only if $$M{\sim_=}R$$. Finally, we characterize all the non-simple weakly virtually divisible modules M for which Ann(M) is a prime ideal and Soc(M) = 0.

Combining SWAT model with artificial neural networks for modelling a daily discharge (일 유출량 해석을 위한 SWAT 모형과 인공신경망의 연계)

  • Lee, Do-Hun;Kim, Nam-Won;Jung, Il-Moon
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.195-195
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    • 2012
  • 인공신경망 모형은 복잡하고 비선형의 입력과 출력 관계를 잘 반영할 수 있어서 유출 모델링에 널리 적용되어 왔다. 그러나 인공신경망 모형은 강우나 유역특성의 공간적 분포를 반영하는 것이 어려우며 물리적 개념이 결여되어 있는 단점이 있다. 본 연구에서는 유역특성과 물리적 개념을 반영할 수 있는 물리기반 모형과 인공신경망 모형의 장점들을 조합하여 물리기반 모형의 일 유출량 해석 능력을 향상하기 위하여 SWAT 모형과 인공신경망(ANN)을 연계하였다. SWAT-ANN 연계모형은 두 단계로 구성되어 진다. 첫 번째 단계에서는 관측 자료를 이용하여 SWAT 모형을 보정한다. 두 번째 단계에서는 첫 번째 단계에서 계산한 소유역별 SWAT 모형의 유출결과를 ANN의 입력자료로 이용하여 SWAT-ANN 연계모형을 구축한다. SCE-UA 최적화 방법을 적용하여 SWAT 모형의 매개변수들을 보정하였고, ANN 학습은 3층의 feed-forward 역전파 알고리즘에 기초한 Bayesian Regularization 방법을 적용하였다. ANN 은닉층의 뉴런 및 전달함수는 시행착오를 통하여 적절한 ANN 구조를 설정하여 SWAT-ANN 연계모형의 일유출량을 모의하였다. 여러 가지 통계적 오차기준을 이용하여 보청천 유역에서 SWAT-ANN 연계모형의 결과와 SWAT 단독 모형의 결과를 비교하였다. SWAT-ANN 연계모형이 SWAT 단독 모형보다 더 우수한 결과를 나타내어 일 유출량 해석을 위한 SWAT-ANN 연계모형의 유용성을 확인할 수 있었다.

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Application of artificial neural network model in regional frequency analysis: Comparison between quantile regression and parameter regression techniques.

  • Lee, Joohyung;Kim, Hanbeen;Kim, Taereem;Heo, Jun-Haeng
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.170-170
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    • 2020
  • Due to the development of technologies, complex computation of huge data set is possible with a prevalent personal computer. Therefore, machine learning methods have been widely applied in the hydrologic field such as regression-based regional frequency analysis (RFA). The main purpose of this study is to compare two frameworks of RFA based on the artificial neural network (ANN) models: quantile regression technique (QRT-ANN) and parameter regression technique (PRT-ANN). As an output layer of the ANN model, the QRT-ANN predicts quantiles for various return periods whereas the PRT-ANN provides prediction of three parameters for the generalized extreme value distribution. Rainfall gauging sites where record length is more than 20 years were selected and their annual maximum rainfalls and various hydro-meteorological variables were used as an input layer of the ANN model. While employing the ANN model, 70% and 30% of gauging sites were used as training set and testing set, respectively. For each technique, ANN model structure such as number of hidden layers and nodes was determined by a leave-one-out validation with calculating root mean square error (RMSE). To assess the performances of two frameworks, RMSEs of quantile predicted by the QRT-ANN are compared to those of the PRT-ANN.

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ANN Sensorless Control of Induction Motor with AFLC Controller (AFLC 제어기에 의한 유도전동기의 ANN 센서리스 제어)

  • Choi, Jung-Sik;Ko, Jae-Sub;Chung, Dong-Hwa
    • The Transactions of the Korean Institute of Power Electronics
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    • v.11 no.3
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    • pp.224-232
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    • 2006
  • The paper proposes the artificial neural network(ANN) sensorless control of induction motor drive with adaptive fuzzy logic controller(AFLC). Also, this paper proposes the speed control of induction motor using AFC and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled AFLC and him controller. And this paper is proposed the results to verify the effectiveness of the AFLC and ANN controller.

A DFT and QSAR Study of Several Sulfonamide Derivatives in Gas and Solvent

  • Abadi, Robabeh Sayyadi kord;Alizadehdakhel, Asghar;Paskiabei, Soghra Tajadodi
    • Journal of the Korean Chemical Society
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    • v.60 no.4
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    • pp.225-234
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    • 2016
  • The activity of 34 sulfonamide derivatives has been estimated by means of multiple linear regression (MLR), artificial neural network (ANN), simulated annealing (SA) and genetic algorithm (GA) techniques. These models were also utilized to select the most efficient subsets of descriptors in a cross-validation procedure for non-linear -log (IC50) prediction. The results obtained using GA-ANN were compared with MLR-MLR, MLR-ANN, SA-ANN and GA-ANN approaches. A high predictive ability was observed for the MLR-MLR, MLR-ANN, SA-ANN and MLR-GA models, with root mean sum square errors (RMSE) of 0.3958, 0.1006, 0.0359, 0.0326 and 0.0282 in gas phase and 0.2871, 0.0475, 0.0268, 0.0376 and 0.0097 in solvent, respectively (N=34). The results obtained using the GA-ANN method indicated that the activity of derivatives of sulfonamides depends on different parameters including DP03, BID, AAC, RDF035v, JGI9, TIE, R7e+, BELM6 descriptors in gas phase and Mor 32u, ESpm03d, RDF070v, ATS8m, MATS2e and R4p, L1u and R3m in solvent. In conclusion, the comparison of the quality of the ANN with different MLR models showed that ANN has a better predictive ability.

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.

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.

ANN Rotor Resistance Estimation of Induction Motor Drive using Multi-AFLC (다중 AFLC를 이용한 유도전동기 드라이브의 ANN 회전자저항 추정)

  • Ko, Jae-Sub;Choi, Jung-Sik;Chung, Dong-Hwa
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.25 no.4
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    • pp.45-56
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    • 2011
  • This paper is proposed artificial neural network(ANN) rotor resistance estimation of induction motor drive controlled by multi-adaptive fuzzy learning controller(AFLC). A simple double layer feedforward ANN trained by the back-propagation technique is employed in the rotor resistance identification. In this estimator, double models of the state variable estimations are used; one provides the actual induction motor output states and the other gives the ANN model output states. The total error between the desired and actual state variables is then back propagated to adjust the weights of the ANN model, so that the output of this model tracks the actual output. When the training is completed, the weights of the ANN correspond to the parameters in the actual motor. The estimation and control performance of ANN and multi-AFLC is evaluated by analysis for various operating conditions. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

KNN/ANN Hybrid Location Determination Algorithm for Indoor Location Base Service (실내 위치기반서비스를 위한 KNN/ANN Hybrid 측위 결정 알고리즘)

  • Lee, Jang-Jae;Jung, Min-A;Lee, Seong-Ro;Song, Iick-Ho
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.48 no.2
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    • pp.109-115
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    • 2011
  • As fingerprinting method, k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighbors k and positions of reference points(RPs). So artificial neural network(ANN) clustering algorithm is applied to improve KNN, which is the KNN/ANN hybrid algorithm presented in this paper. For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. In the proposed algorithm, through KNN, k RPs are firstly chosen as the data samples of ANN based on SNR. Then, the k RPs are classified into different clusters through ANN based on SNR. Experimental results indicate that the proposed KNN/ANN hybrid algorithm generally outperforms KNN algorithm when the locations error is less than 2m.