@article {390,
	title = {Algorithm research for supply chain demand prediction - Taking fresh agricultural product enterprises as example},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {160 - 164},
	abstract = {Supply chain demand prediction plays a very important role for enterprises to realize sales and markets management target effectively, especially for fresh agricultural product enterprises. A new model for supply chain demand prediction for fresh agricultural product enterprises is presented based on improved BP neural network. First the advantages and disadvantages of BP neural network algorithm are analyzed when it is used in supply chain demand prediction; Second, Legendre wavelets algorithm is used to speed up the convergence and improve the prediction accuracy of original BP neural network algorithm and based on this the paper advances a new supply chain demand prediction model for fresh agricultural product enterprises. Finally, certain fresh agricultural product enterprise is taken for example to verify the validity and feasibility of the model and the experimental results show that the model can improve prediction accuracy and decrease the calculation time and can be used for supply chain demand prediction practically.},
	keywords = {BP Neural Network, Legendre wavelets, Markets management, Supply chain demand prediction},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84879120765\&partnerID=40\&md5=78a05f213a7f4f86b41f6e014b4c897b},
	author = {Wang, H.}
}
@article {425,
	title = {The modeling of genetic and Tabu search algorithm based BP neural network in the risk analysis of investment},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Cited By (since 1996):2Export Date: 10 July 2014},
	month = {2013},
	pages = {391 - 399},
	abstract = {There are many parameters which affect the economic benefit indicators for evaluation and they are all uncertain. Because of the parameters{\textquoteright} uncertainty, evaluation indicators values of economic benefit in project investment are also uncertain. In these years, more and more BP Neural network based models are established to evaluate the risk of investment for its characters of wide information distribution and strong fault tolerance. However, the internal defect of local minima problem in this model will greatly discount the effects of evaluation if the training data are not proposed selected. To solve this problem of traditional BP neural network, an optimization algorithm that combines the advantages of genetic algorithm (GA) and Tabu Search (TS) is proposed. The training process is divided into two phases in order to search promising parameters of the BP neural network in favor of better evaluation and analysis of the investment. In the first phase, the initial parameters are first searched by GA algorithm by taking advantage of its various searches of the solutions, and the best parameters are selected by Tabu search algorithm in the second phases. After calculation of practical data, it proves that the new algorithm has better convergence rate and predictive accuracy, which make the evaluation and risk analysis of the investment faster and more accurate.},
	keywords = {BP Neural Network, Genetic Algorithm, Investment Evaluation, Risk Analysis, Tabu Search Algorithm},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84892147727\&partnerID=40\&md5=33572128b86e13bfcb8fb0b8e2c32994},
	author = {Yang, L. and Yunlong, D.}
}
@article {373,
	title = {The short-term wind power forecast based on phase-space reconstruction and neural networks},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2013},
	pages = {40 - 45},
	abstract = {The short-term forecast of the wind power of a wind farm is of great significance for the security and stability of a grid-connected generation system. An accurate forecast may reduce the spinning reserve of a grid while providing reliable references for operation dispatch of a wind farm. In order to improve the accuracy of short-term forecasts, introducing the phase-space reconstruction technique of the chaos theory, this paper was established forecasting models by reconstructing the historical time-series data of the wind power of a single unit based on the dynamical properties of chaos sequences, choosing the best delay time with the mutual information method, determining the best combination of embedding dimensions with the Cao algorithm, as well as utilizing the Elman recurrent neural network and others like the BP. As comparative case analysis shows, the forecasts of Elman model are more accurate than that of the others, exhibiting a positive prospect of utilizing this combined model of phase-space reconstruction and neural network in wind power forecasting of a single unit.},
	keywords = {BP Neural Network, Delay time, Elman neural network, Embedding dimensions, Phase-space reconstruction, The short-term wind power forecasts},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84879073309\&partnerID=40\&md5=fdd64e3c0ae9db677a30cd0fc318f9ba},
	author = {Zhang, J.-H. and Liu, Y.-Q. and Tian, D.}
}
@article {343,
	title = {A new customer classification algorithm for electronic commerce enterprises},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Export Date: 10 July 2014},
	month = {2012},
	pages = {284 - 288},
	abstract = {Correctly and effectively customer classification according to their characteristics and behaviors will be the most important resource for electronic marketing and online trading of network enterprises. A new customer classification algorithm for electronic commerce enterprises is advanced based on analyzing customer characteristics and behaviors. First, based on consumer characteristics and behavior analysis, 21 customer classification indicators, including customer characteristics type variables and customer behavior type variables, are designed, Second, aiming at the shortages of the existing BP neural network algorithm of data-mining for customer classification, the immune-genetic algorithm is used to correct BP neural network to speed up the convergence of the model. Finally the experimental results verify that the new algorithm can improve customer classification accuracy and can guarantee the effectiveness and validity of customer classification for electronic commerce enterprises in its engineering application.},
	keywords = {BP Neural Network, Customer classification, Electronic commerce, Immune genetics},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84870801373\&partnerID=40\&md5=e0e9cd1f8a408b64aa0f9dc6037ec8e6},
	author = {Li, X.}
}
@article {351,
	title = {Risk evaluation of rural financial organization operation based on the BP},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Export Date: 10 July 2014},
	month = {2012},
	pages = {341 - 346},
	abstract = {The rural financial organization plays an important role in promoting the healthy development of the national economy especially the development of the rural economy. However, the rural financial organization risk especially the operating risk is still an outstanding problem. It has become a big obstacle in new countryside development. In this paper we analyzes the rural financial organization operational risk index firstly, then use these index as the input data for the BP neural network. After that we modeling rural financial organization operational risk early warning based the BP neural network. At last we take an experiment on a rural somewhere, the result shows that the BP neural network can reflect of the operation risk of the rural financial organization quickly and accurately.},
	keywords = {BP Neural Network, Risk early warning, Risk evaluation, Rural finanical},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84870856910\&partnerID=40\&md5=85d32baadc606adc5051d555a7003b8a},
	author = {Fan, S. and Zhao, Y. and Xie, H. and Li, C.}
}
@article {300,
	title = {Application research on bp neural network pid control of the belt conveyor},
	journal = {Journal of Digital Information Management},
	volume = {9},
	year = {2011},
	note = {Export Date: 10 July 2014},
	month = {2011},
	pages = {266 - 270},
	abstract = {In order to control reasonably the acceleration and dynamic tension in full-load starting process of conveyor system, accurate design techniques should be adopted. This paper selects the viscoelasticity model suiting to the belt by comparing dynamic performance of some kinds of models, and develops simulation model of driving device with feedback control, which indicating that motor rotation speed and its output torque vary with load and time. For belt conveyors, it has high nonlinear trait, PID controller with fixed parameter can not achieve good performance index. So the PID controller based on neural network is proposed. This design regards belt conveyors as a control object, by analyzing danamic model of belt conveyors, the design of related neural network PID control system based on BP neural network is provided. The BP-ANN is designed with three layers which are input-layer, hidden-layer and output layer in consideration of the control system real time requirement. The results show that the controller based on the neural networks can improve the robustness of the system and has better adaptabilities to the model and environments, compared with the classical PID control, therefore the simulation results are more close to practice.},
	keywords = {Belt Conveyor, BP Neural Network, Dynamic model, PID controller},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84855380085\&partnerID=40\&md5=8f75d8ae8fabd7ffd683a7b47b40c795},
	author = {Xi, P. and Song, Y.}
}
