01940nam a2200253   450000500170000000800410001703500150005810000190007324500670009226000520015930000140021149000260022550001410025150200570039252008750044965000390132465000160136370000360137970000470141570000480146271000530151081000580156385600650162120260817162835.0030698         th                  eng    a.b100874000 aJong Jek Siang10aApplication of back propagation method in forecasting problems  aBangkok :bAsian Institute of Technology,c1992  a65 leaves1 aThesis ;vno. CS-92-9  aA thesis submitted in partial fulfillment of the requirements for  the degree of Master of Science, School of Engineering and Technology  aThesis (M.Sc.) - Asian Institute of Technology, 1992  aIn this study, monthly water quality (Temperature, pH,  Conductivity) at Vientiane, Laos, and monthly water flows at  Vientiane, Ubon, Yasothon, Wat-Tai Kosum and Tha Sang Kran Bridge  are forecast one month ahead, using Back Propagation method without  other external data. It was found that for seasonal data like water  flows, small network (one hidden layer with one to three units in  it) is enough to learn the pattern data. Additional hidden units do  not improve the performance significantly. The results show also  that generally, forecasting using Back Propagation method gives  better results than using the average data in each month, and using  data in the same month of the previous year. In addition, the  result of Vientiane station shows also that forecasting using Back  Propagation model gives a better results as compared to the Box-Jenkins method.  0aNeural networks (Computer science) 0aForecasting1 aHuynh, Ngoc Phien,eChairperson1 aHosomura, Tsukasa, eExamination Committee1 aSadananda, Ramakoti,eExamination Committee2 aThe Government of Australia, eScholarship Donor2 aAsian Institute of Technology.tThesis ;vno. CS-92-9  3Full-Textuhttp://203.159.5.9/ait-thesis/detail.php?q=B16612