• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.09a
• /
• pp.157-159
• /
• 2005

• Advances in environmental research
• /
• v.4 no.4
• /
• pp.219-231
• /
• 2015

In recent years, raising air pollutants has become as a big concern, especially in metropolitan cities such as Tehran. Therefore, forecasting the level of pollutants plays a significant role in air quality management. One of the forecasting tools that can be used is an artificial neural network which is able to model the complicated process of air pollution. In this study, we applied two different methods of artificial neural networks, the Multilayer Perceptron (MLP) and Radial Basis Function (RBF), to predict the hourly air concentrations of toluene in Tehran. Hourly temperature, wind speed, humidity and $NO_x$ were selected as inputs. Both methods had acceptable results; however, the RBF neural network produced better results. The coefficient of determination ($R^2$) between the observed and predicted data was 0.9642 and 0.99 for MLP and RBF neural networks, respectively. The results of the mean bias errors (MBE) were 0.00 and -0.014 for RBF and MLP, respectively which indicate the adequacy of the models. The index of agreement (IA) between the observed and predicted data was 0.999 and 0.994 in the RBF and the MLP, respectively which indicates the efficiency of the models. Finally, sensitivity analysis related to the MLP neural network determined that temperature was the most significant factor in air concentration of toluene in Tehran which may be due to the volatile nature of toluene.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.8 no.3
• /
• pp.185-191
• /
• 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.

• KIPS Transactions on Software and Data Engineering
• /
• v.7 no.12
• /
• pp.485-496
• /
• 2018

So far, the deep learning, a field of artificial intelligence, has achieved remarkable results in solving problems from unstructured data. However, it is difficult to comprehensively judge situations like humans, and did not reach the level of intelligence that deduced their relations and predicted the next situation. Recently, deep neural networks show that artificial intelligence can possess powerful relational reasoning that is core intellectual ability of human being. In this paper, to analyze and observe the performance of Relation Networks (RN) among the neural networks for relational reasoning, two types of RN-based deep neural network models were constructed and compared with the baseline model. One is a visual question answering RN model using Sort-of-CLEVR and the other is a text-based question answering RN model using bAbI task. In order to maximize the performance of the RN-based model, various performance improvement experiments such as hyper parameters tuning have been proposed and performed. The effectiveness of the proposed performance improvement methods has been verified by applying to the visual QA RN model and the text-based QA RN model, and the new domain model using the dialogue-based LL dataset. As a result of the various experiments, it is found that the initial learning rate is a key factor in determining the performance of the model in both types of RN models. We have observed that the optimal initial learning rate setting found by the proposed random search method can improve the performance of the model up to 99.8%.

• Proceedings of the KIEE Conference
• /
• 2000.11d
• /
• pp.621-623
• /
• 2000

In this paper, the effective design method of the predictive controller using fuzzy neural networks(FNNs) is presented for the Intelligent control of chaotic nonlinear systems. In our design method of controller, predictor parameters are tuned by the error value between the actual output of a chaotic nonlinear system and that of a fuzzy neural network model. And the parameters of predictive controller using fuzzy neural network are tuned by the gradient descent method which uses control error value between the actual output of a chaotic nonlinear system and the reference signal. In order to evaluate the performance of our controller, it is applied to the Duffing system which are the representative continuous-time chaotic nonlinear systems and the Henon system which are representative discrete-time chaotic nonlinear systems.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.2 no.1
• /
• pp.19-26
• /
• 1998

In this paper, Simulation result was studied by PID controller in series to the estblised neural networks controller. Neural network model is composed of two layers to evaluate tracking performance improvement. The regular dynamics was also studied for the expected error to be minimized by using Widrow-Hoff delta rule. As a result of the study, We identified that tracking performance improvement was developed more in case of connecting PID than conventional neural network controller and that tracking plant parameter in 251 sample was approached rapidly in case of time varying.

• Journal of the Korea Society of Computer and Information
• /
• v.6 no.3
• /
• pp.29-34
• /
• 2001

Competition is increasing in telecommunication service market. Effective customer retention strategies are based on a clear understanding of customer defection. Data mining offers service providers great opportunities to get closer to customer. In this paper, we propose an efficient data mining algorithm using neural network. Especially Analysis of CRM Using Neural Networks in Telecommunication service Market and a practical application of neural network is described telco, churn management This paper builds model of customer defection management and analyzes customer defection with data mining

• The Korean Journal of Applied Statistics
• /
• v.32 no.4
• /
• pp.573-591
• /
• 2019

Classification criteria for Korean alphabet (Hangul) fonts are undeveloped in comparison to numerical classification systems for Roman alphabet fonts. This study finds important features that distinguish typeface styles in order to help develop numerical criteria for Hangul font classification. We find features that determine the characteristics of the two different styles using a convolutional neural network to create a model that analyzes the learned filters as well as distinguishes between serif and sans-serif styles.

• Structural Engineering and Mechanics
• /
• v.84 no.1
• /
• pp.35-48
• /
• 2022

This paper investigates the artificial neural network (ANN) to predict the dimensionless parameters for contact pressures and contact lengths under the rigid punch, the initial separation loads, and the initial separation distances of a contact problem. The problem consisted of two elastic infinitely layers (EL) loaded by means of a rigid cylindrical punch and resting on a half-infinite plane (HP). Firstly, the problem was formulated and solved theoretically using the Theory of Elasticity (ET). Secondly, the contact problem was extended based on the ANN. External load, the radius of punch, layer heights, and material properties were created by giving examples of different values used at the training and test stages of ANN. Finally, the accuracy of the trained neural networks for the case was tested using 134 new data, generated via ET solutions to determine the best network model. ANN results were compared with ET results, and well agreements were achieved.

• Journal of the Korean Society for Precision Engineering
• /
• v.14 no.4
• /
• pp.110-118
• /
• 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.