01718nas a2200181   4500008004100000245011700041210006900158260000900227300001400236490000700250520104700257653002201304653002201326653002301348653003501371100001301406856011701419       2013                        eng d00aAlgorithm research for supply chain demand prediction - Taking fresh agricultural product enterprises as example0 aAlgorithm research for supply chain demand prediction Taking fre  c2013  a160 - 1640 v113 aSupply 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.10aBP Neural Network10aLegendre wavelets10aMarkets management10aSupply chain demand prediction1 aWang, H.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84879120765&partnerID=40&md5=78a05f213a7f4f86b41f6e014b4c897b02104nas a2200205   4500008004100000245011300041210006900154260000900223300001400232490000700246520138500253653002201638653002201660653002601682653001801708653002601726100001301752700001601765856011701781       2013                        eng d00aThe modeling of genetic and Tabu search algorithm based BP neural network in the risk analysis of investment0 amodeling of genetic and Tabu search algorithm based BP neural ne  c2013  a391 - 3990 v113 aThere are many parameters which affect the economic benefit indicators for evaluation and they are all uncertain. Because of the parameters' 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.10aBP Neural Network10aGenetic Algorithm10aInvestment Evaluation10aRisk Analysis10aTabu Search Algorithm1 aYang, L.1 aYunlong, D.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84892147727&partnerID=40&md5=33572128b86e13bfcb8fb0b8e2c3299401885nas a2200229   4500008004100000245009500041210006900136260000900205300001200214490000700226520110200233653002201335653001501357653002501372653002501397653003101422653004001453100001701493700001501510700001301525856011701538       2013                        eng d00aThe short-term wind power forecast based on phase-space reconstruction and neural networks0 ashortterm wind power forecast based on phasespace reconstruction  c2013  a40 - 450 v113 aThe 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.10aBP Neural Network10aDelay time10aElman neural network10aEmbedding dimensions10aPhase-space reconstruction10aThe short-term wind power forecasts1 aZhang, J.-H.1 aLiu, Y.-Q.1 aTian, D.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84879073309&partnerID=40&md5=fdd64e3c0ae9db677a30cd0fc318f9ba01663nas a2200181   4500008004100000245008000041210006900121260000900190300001400199490000700213520103900220653002201259653002801281653002401309653002001333100001101353856011701364       2012                        eng d00aA new customer classification algorithm for electronic commerce enterprises0 anew customer classification algorithm for electronic commerce en  c2012  a284 - 2880 v103 aCorrectly 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.10aBP Neural Network10aCustomer classification10aElectronic commerce10aImmune genetics1 aLi, X.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84870801373&partnerID=40&md5=e0e9cd1f8a408b64aa0f9dc6037ec8e601487nas a2200217   4500008004100000245007800041210006900119260000900188300001400197490000700211520080100218653002201019653002301041653002001064653002001084100001201104700001301116700001201129700001101141856011701152       2012                        eng d00aRisk evaluation of rural financial organization operation based on the BP0 aRisk evaluation of rural financial organization operation based   c2012  a341 - 3460 v103 aThe 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.10aBP Neural Network10aRisk early warning10aRisk evaluation10aRural finanical1 aFan, S.1 aZhao, Y.1 aXie, H.1 aLi, C.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84870856910&partnerID=40&md5=85d32baadc606adc5051d555a7003b8a01890nas a2200193   4500008004100000245007900041210006900120260000900189300001400198490000600212520126000218653001801478653002201496653001801518653001901536100001101555700001301566856011701579       2011                        eng d00aApplication research on bp neural network pid control of the belt conveyor0 aApplication research on bp neural network pid control of the bel  c2011  a266 - 2700 v93 aIn 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.10aBelt Conveyor10aBP Neural Network10aDynamic model10aPID controller1 aXi, P.1 aSong, Y.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84855380085&partnerID=40&md5=8f75d8ae8fabd7ffd683a7b47b40c795