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1830 JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER 2011 p01=p1(5:7)'; y01=sim(net,p01); y08=min(p0)+y01*(max(p0)-min(p0)); E01=(p0(8)-y08)/p0(8); p02=p1(6:8)'; y02=sim(net,p02); y09=min(p0)+y02*(max(p0)-min(p0)); E02=(p0(9)-y09)/p0(9); p03=p1(7:9)'; y03=sim(net,p03); y10=min(p0)+y03*(max(p0)-min(p0)); E03=(p0(10)-y10)/p0(10); y=[y08 E01;y09 E02;y10 E03] p04=p1(8:10)'; y04=sim(net,p04); y11=min(p0)+y04*(max(p0)-min(p0)) p2=[p0 y11]; day1=[day 2009]; figure plot(day, p0,'g-.') hold on plot(day, p0,'b+') hold on plot(2006,y08,'ro') plot(2007,y09,'ro') plot(2008,y10,'ro') plot(2009,y11,'ro') plot(day1,p2, 'k-.') Table 6 shows the details about the prediction of the location quotient of China's provincial manufacturing. table 6: Result of BP neural network prediction (2006-2009) 2006 2007 2008 2009 Provinces Actual Predicted Relative Actual Predicted Relative Actual Predicted Relative Predicted value value error value value error value value error value Beijing 1.3471 1.3471 0.0000 1.2995 1.3513 -0.0399 1.3317 1.3417 -0.0076 1.3234 Tianjin 3.0678 3.0680 -0.0001 3.3412 3.2362 0.0314 3.1160 3.1768 -0.0195 3.3121 Hebei 0.9264 0.9268 -0.0004 0.9620 0.9702 -0.0085 0.9860 0.9975 -0.0117 0.9678 Shanxi 1.1078 1.1077 0.0001 1.1452 1.1093 0.0314 1.1828 1.1927 -0.0084 1.2705 Inner Mongolia 0.8289 0.8293 -0.0004 0.8529 0.8756 -0.0266 0.8782 0.8837 -0.0063 0.9291 Liaoning 1.3766 1.3741 0.0019 1.4282 1.3442 0.0589 1.4928 1.5446 -0.0347 1.7410 Jilin 0.9328 0.9325 0.0004 0.9951 1.0093 -0.0142 1.0031 1.0477 -0.0445 1.1994 Heilongjiang 0.5891 0.5921 -0.0052 0.6210 0.5937 0.0440 0.6533 0.6673 -0.0215 0.7849 Shanghai 3.5631 3.5114 0.0145 3.8370 3.8636 -0.0069 4.0874 4.0309 0.0138 4.0215 Jiangsu 2.3332 2.3264 0.0029 2.4230 2.4105 0.0052 2.5108 2.4587 0.0207 2.4824 Zhejiang 2.1312 2.1315 -0.0002 2.1339 2.2604 -0.0593 2.2352 2.2784 -0.0193 2.2766 Anhui 0.4583 0.4587 -0.0008 0.4807 0.4570 0.0492 0.5093 0.4927 0.0326 0.4968 Fujian 1.2987 1.2985 0.0002 1.3475 1.3451 0.0018 1.3897 1.3361 0.0386 1.3027 Jiangxi 0.6510 0.6511 -0.0001 0.6724 0.6739 -0.0022 0.7028 0.6804 0.0318 0.6711 Shandong 1.4882 1.4889 -0.0005 1.5514 1.5563 -0.0031 1.6114 1.5556 0.0346 1.5575 Henan 0.5783 0.5780 0.0005 0.6038 0.5920 0.0196 0.6261 0.6312 -0.0081 0.6476 Hubei 0.8619 0.8687 -0.0079 0.8907 0.9224 -0.0356 0.9010 0.9064 -0.0060 0.8571 Hunan 0.6870 0.6870 0.0000 0.7222 0.7075 0.0205 0.7530 0.7612 -0.0110 0.7743 Guangdong 2.6261 2.6265 -0.0001 2.7000 2.7044 -0.0016 2.8909 2.9614 -0.0244 2.9368 Guangxi 0.3574 0.3576 -0.0006 0.3706 0.3692 0.0036 0.3812 0.3970 -0.0413 0.4505 Hainan 0.2636 0.2641 -0.0019 0.2611 0.2734 -0.0472 0.2768 0.2730 0.0139 0.2851 Sichuan 0.6065 0.6068 -0.0005 0.6266 0.6306 -0.0064 0.6456 0.6691 -0.0364 0.6766 Guizhou 0.3089 0.3089 0.0002 0.3182 0.3157 0.0078 0.3310 0.3386 -0.0227 0.3437 Yunnan 0.3094 0.3111 -0.0055 0.3119 0.2997 0.0389 0.3156 0.3227 -0.0224 0.3359 Xizang 0.1206 0.1207 -0.0004 0.1162 0.1125 0.0318 0.1152 0.1125 0.0234 0.1125 Shaanxi 0.6657 0.6649 0.0011 0.7090 0.6814 0.0389 0.7447 0.7702 -0.0342 0.6921 Gansu 0.5786 0.5789 -0.0005 0.6076 0.5766 0.0511 0.6346 0.6518 -0.0272 0.6594 Qinghai 0.4761 0.4763 -0.0004 0.4896 0.4783 0.0231 0.5105 0.5374 -0.0527 0.5895 Ningxia 0.7761 0.7792 -0.0039 0.8054 0.8101 -0.0058 0.8593 0.8469 0.0145 0.8456 Xinjiang 0.4726 0.4714 0.0026 0.4837 0.4655 0.0376 0.4990 0.4868 0.0244 0.4834 We have high precision in the prediction using BP neural network (Relative errors are within 6%). This also shows that BP neural network has a strong function in learning, association, fault-tolerant and highly nonlinear function mapping with a good ability of generalization. © 2011 ACADEMY PUBLISHER ACKNOWLEDGMENT The authors are grateful to Professor Huanming Zhang and Professor Erpo Lu of Anhui University of Financial and Economics (AUFE) for their valuable comments. The authors are also grateful to Zhongsheng Xu, a graduate student of department of statistics of AUFE for our earlier joint work. However, any remaining errors are the author’s responsibility. This work was supported in part by a grant from XYZ.
JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER 2011 1831 REFERENCES [1] Anselin L. Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic, 1988. [2] Anselin L. “Space and applied econometrics”. Special Issue, Regional Science and Urban Economics, Vol. 22, No. 3, pp.509-536, 1992. [3] Anselin L, Florax R. “Small sample properties of tests for spatial dependence in regression models: some further results”. In New Directions in Spatial Econometrics, Edited by Anselin and Florax. Berlin: Springer-Verlag, 1995, pp.21–74. [4] Anselin L. GeoDa 0.9.3 User's Guide. Center for Spatially Integrated Social Science, 2003. [5] Mohamad H. Hassoun. Fundamentals of Artificial Neural Networks. The MIT Press, 1995. [6] Saeed Moshiri and Norman Cameron. “Neural Network Versus Econometric Models in Forecasting Inflation”. Journal of Forecasting, No. 19, pp.201-217, 2000. [7] Paul Krugman. Geography and Trade. The MIT Press, 1992. [8] G. Ellison and E. Glaeser. “Geographic concentration in US manufacturing industries: A dartboard approach”. Journal of Political Economy, Vol. 105, No. 5, pp.889–927, 1997. © 2011 ACADEMY PUBLISHER [9] Marius Brulhart. “Economic Geography, Industry location and trade: the evidence”. The World Economy, Vol. 21, No. 6, pp.775-801, 1998. [10] Antje. “Determinations of Geographical concentration patterns in central and eastern European countries”, unpublished. [11] Stuart S. Rosenthal. “The determinants of agglomenration”. Journal of Urban, Vol. 50, No. 2, pp.191-229, 2001. [12] Kim, S. “Expansion of Markets and the Geographic Distribution of Economic Activities: The Trends in U.S. Regional Manufacturing Structure, 1860-1987”. Quarterly Journal of Economics, Vol. 110, pp.881-908, 1995. Huayin Yu (1962-), professor in Department of Statistics of Anhui University of Financial and Economics, master tutor. Professor Huayin Yu majors in numerical calculation and data analysis. He is the corresponding author of this article. Weiping Gu (1985-), graduate student in Department of Statistics of Anhui University of Financial and Economics. He majors in numerical calculation and data analysis.
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