• Journal of Information Processing Systems
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• v.15 no.2
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• pp.305-319
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• 2019

The prediction of the sum of container is very important in the field of container transport. Many influencing factors can affect the prediction results. These factors are usually composed of many variables, whose composition is often very complex. In this paper, we use gray relational analysis to set up a proper forecast index system for the prediction of the sum of containers in foreign trade. To address the issue of the low accuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factors and other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP) neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalized by the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residual correction calculation for the results based on the preliminary data. The results of practical examples show that the overall relative error of the combined prediction model is no more than 1.5%, which is less than the relative error of the single prediction models. It is hoped that the research can provide a useful reference for the prediction of the sum of container and related studies.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.98-107
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• 2015

Hierarchical constraints and constraint satisfaction were analyzed in order to solve the problem of conflict detection in collaborative design. The constraints were divided into two sets: one set consisted of known constraints and the other of unknown constraints. The constraints of the two sets were detected with corresponding methods. The set of the known constraints was detected using an interval propagation algorithm, a back propagation (BP) neural network was proposed to detect the set with the unknown constraints. An immune algorithm (IA) was utilized to optimize the weights and the thresholds of the BP neural network, and the steps were designed for the optimization process. The results of the simulation indicated that the BP neural network that was optimized by IA has a better performance in terms of convergent speed and global searching ability than a genetic algorithm. The constraints were described using the eXtensible Markup Language (XML) for computers to be able to automatically recognize and establish the constraint network. The implementation of the conflict detection system was designed based on constraint satisfaction. A wind planetary gear train is taken as an example of collaborative design with a conflict detection system.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.1-10
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• 2021

In the past, designers relied primarily on past experience and reference to industry standard thresholds to design spaces. Such design often results in spaces that do not meet the needs of users. The purpose of this paper is to investigate the process and way of generating design parameters by constructing a BP neural network algorithm for spatial design. From the perspective. This paper adopts an experimental research method to take a kindergarten with a large number of complex needs in space as the object of study, and through the BP neural network algorithm in machine learning, the correlation between environmental behavior parameters and spatial design parameters is imprinted. The way of generating spatial design parameters is studied. In the future, the corresponding spatial design parameters can be derived by replacing specific environmental behavior influence factors, which can be applied to a wider range of scenarios and improve the efficiency of designers.

• Journal of the Korean GEO-environmental Society
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• v.10 no.5
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• pp.49-57
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• 2009

In this study, one of the complicated geotechnical problem, compression index was predicted by a artificial neural network method of Levenberg-Marquardt (LM) algorithm. Predicted values were compared and evaluated by the results of the Back Propagation (BP) method, which is used extensively in geotechnical engineering. Also two different results were compared with experimental values estimated by verified experimental methods in order to evaluate the accuracy of each method. The results from experimental method generally showed higher error than the results of both artificial neural network method. The predicted compression index by LM algorithm showed better comprehensive results than BP algorithm in terms of convergence, but accuracy was similar each other.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.4
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• pp.89-96
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• 2002

This paper aims to develop an intelligent model for predicting top-bead width for the robotic GMA(Gas Metal Arc) welding process using BP(Back-propagation) neural network and multiple regression analysis. Firstly, based on experimental data, the basic factors affecting top-bead width are identified. Then BP neural network model and multiple regression models of top-bead width are established. The modeling methods and procedure are explained. The developed models are then verified by data obtained from the additional experiment and the predictive behaviors of the two kind of models are compared and analysed. Finally the modeling methods, predictive behaviors md the advantages of each models are discussed.

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.2
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• pp.105-127
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• 2003

In this paper, four different data mining techniques, two neural networks and two statistical modeling techniques, are compared in terms of prediction accuracy in the context of bankruptcy prediction. In business setting, how to accurately detect the condition of a firm has been an important event in the literature. In neural networks, Backpropagation (BP) network and the Kohonen self-organizing feature map, are selected and compared each other while in statistical modeling techniques, discriminant analysis and logistic regression are also performed to provide performance benchmarks for the neural network experiment. The findings suggest that the BP network is a better choice among the data mining tools compared. This paper also identified some distinctive characteristics of Kohonen self-organizing feature map.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.989-995
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• 2017

As is known to all that the output of wind power generation has a character of randomness and volatility because of the influence of natural environment conditions. At present, the research of wind power prediction mainly focuses on point forecasting, which can hardly describe its uncertainty, leading to the fact that its application in practice is low. In this paper, a wind power range prediction model based on the multiple output property of BP neural network is built, and the optimization criterion considering the information of predicted intervals is proposed. Then, improved Particle Swarm Optimization (PSO) algorithm is used to optimize the model. The simulation results of a practical example show that the proposed wind power range prediction model can effectively forecast the output power interval, and provide power grid dispatcher with decision.

• Journal of Information Processing Systems
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• v.13 no.1
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• pp.141-151
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• 2017

Effective identification of wireless channel in different scenarios or regions can solve the problems of multipath interference in process of wireless communication. In this paper, different characteristics of wireless channel are extracted based on the arrival time and received signal strength, such as the number of multipath, time delay and delay spread, to establish the feature vector set of wireless channel which is used to train backpropagation (BP) neural network to identify different wireless channels. Experimental results show that the proposed algorithm can accurately identify different wireless channels, and the accuracy can reach 97.59%.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.705-709
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• 1995

The objective of this study is to design a multi-layer neural network which controls the position of excavator's attachment. In this paper, a dynamic controller has been developed based on an error back-propagation(BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it was used as a commanded feedforward input generator. A PD feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the excavator as well as the PD feedback error. By using the BP network as a feedforward controller, no a priori knowledge on system dynamics is need. Computer simulation results demonstrate such powerful characteristics of the proposed controller as adaptation to changing environment, robustness to disturbancen and performance improvement with the on-line learning in the position control of excavator attachment.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.328-330
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• 2004

In this paper, ECG based cardiac disease diagnosis models are developed. Conventionally, ECG monitoring equipments can only measure and store ECG signals and they always require medical doctor's diagnosis actions which are not desirable for continuous ambulatory monitoring and diagnosis healthcare systems. In this paper, two kinds of neural based self cardiac disease diagnosis engines are developed and tested for four kinds of diseases, sinus bradycardia, sinus tachycardia, left bundle branch block and right bundle branch block. For diagnosis engines, error backpropagation neural network (BP) and probabilistic neural network (PNN) were applied. Five signal features including heart rate, QRS interval, PR interval, QT interval, and T wave types were selected for diagnosis characteristics. To show the validity of proposed diagnosis engine, MIT-BIH database were used to test. Test results showed that BP based diagnosis engine has 71% of diagnosis accuracy which is superior to accuracy of PNN based diagnosis engine. However, PNN based diagnosis engine showed superior diagnosis accuracy for complex-disease diagnoses than BP based diagnosis engine.