How to perform nonlinear regression accross multiple datasets using MATLAB? I am trying to fit a model to mutiple data sets at once using non linear regression (by writing a suitable MATLAB programme. The model contains 3 unkown paramaters that must be tuned to satsifty (or give best model fit) accross 4 data sets at once. However, the model also con- tains 1 known paramater which is different for each of the 4 datasets. Model to fit: ARON Ron = + A2 In (1+ . ARon/Ron are the data set y values t is the data set x values A1, A2, y are unkown paramaters (common to all data sets) which must be found tau is a kown paramaer whcih differs accross all data sets . www wwwwwwwwwwwwwwwwwwwwwww Relavent information/data: tau = (3.10755e-19, 3.36127e-19, 3.22524E-19, 3.15416E-19), for datasets 1, 2, 3 and 4 re- spectively A1, A2 & gamma are unkown fitting paramaters (common to all data sets) $tau = (3.10755e-19, 3.36127e-19, 3.22524E-19, 3.15416E-19), for datasets 1, 2, 3 and 4 respectively & Al, A2 & gamma are unkown fitting paramaters (common to all data sets) Dataset 1 Dataset 2 Dataset 3 Dataset 4 X ly X ly Y Y 0 0 0.4182269741 0.509283834 0.566780287 0.618508886 0.650175014 0.691427136 0.714707096 ***** 0.739713491 ... 0.7879871481 0.00E+00 3.60E+03 7.21E-03 1.08E+04 1.44E+04 1.80E+04 2.16E+04 406 2.52E+04 2.88E+04 3.24E+04 3.60E+04 3.96E+04 4.32E+04 VELU 4.68E+04 5.04E+04 5.41E+04 5.77E+04 un 6.13E+04 du 6.49E+04 *** 6.85E+04 7.21E+04 ca 7.57E+04 7.93E+04 8.29E+04 8.65E+04 9.01E+04 9.37E+04 9.73E+04 1.01E+05 1.05E+05 1 OEEOE 1.08E+05 1 ORE. 110.B 1.12E+05 115.B 1.15E+05 1.19E+05 19ELE 122. 1.23E+05 125. 1.26E+05 1.30E+05 30.05 1.33E+05 1.37E+05 1.41E+05 0 0.850149996 1.19401382 1.470331057 1.717810636 1.951372271 ---- 2.180596328 ---- 2.363314416 *** 2.57788858 20020 2.781997996 **222*120 2.983129546 3.135282349 *** 3.304981827 102" 3.498450329 3.660977645 3.826092999 ***** 3.97241754 4.131648404 ve 4.272628003 4.432203018 w 4.569917484 10 4.701389988 0104-10 4.848194103 ---- 4.979318686 5.112661028 --- 5.253893067 5.390520777 5.520699905 594527044 5.624577911 5 749299901 5.748288891 BA050745 5.84950746 072310701 5.974319491 6097844507 6.087844507 C2000FZ57 6.20895757 6.335073822 6.436503398 6.531236171 6.61988729 6.73626975 6.859356225 0.00E+00 3.60E+03 7.21E+03 1.08E+04 1.44E+04 1.80E+04 2.16E+04 2.52E+04 2.88E+04 3.24E+04 *** 3.60E+04 3.96E+04 4.32E+04 4.68E+04 5.04E+04 5.41E+04 5.77E+04 e 6.13E+04 6.49E+04 6.85E+04 7.21E+04 7.57E+04 7.93E+04 8.29E+04 8.65E+04 9.01E+04 9.37E+04 9.73E+04 101.5 1.01E+05 1.04E+05 1.08E+05 RESOS 1.12E+05 1.15E+05 1 195205 1.19E+05 1.23E+05 1.26E+05 1.30E+05 1.33E+05 1.37E+05 1.41E+05 1.12465595 1.346980933 1.516458238 1.688253375 1.810110851 1.940857975 --- 2.067912147 ---- 2.167128124 - 2.2614281041 re 2.3530563 2.436567903 2.51467621 2012 2.617319793 ---- 2.712319122 ---- 2.804267945 2.887144632) - 2.958025421 ---- 3.037489696 2020 3.11890248 ---- 3.223051539 3.256562863 3.326180135 3.364235794 3.44037934 3.513280566 3.577158241 3.657893056 3.746943763 3.817675937 258617017 3.868647917 3.950778165 3.9369875241 4.000045553 4.055346407 A138745074 4.133746074 4.200906938 4.243181048 4.328056347 4.40089188 0.00E+00 3.60E+03 7.21E+03 1.08E+04 1.44E+04 1.80E+04 2.16E+04 2.52E+04 2.88E+04 3.24E+04 3.60E+04 3.96E+04 4.32E+04 4.68E+04 5.04E+04 5.41E+04 va 5.77E+04 Surre 6.13E+04 6.49E+04 6.85E+04 7.21E+04 7.57E+04 7.93E+04 8.29E+04 8.65E+04 9.01E+04 9.37E+04 9.73E+04 1017-05 1.01E+05 1.04E+05 1.08E+05 1.12E+05 1.15E+05 1.19E+05 + 1.23E+05 1.26E+05 1.30E+05 + 1.33E+05 1.37E+05 1.41E+05 0.854599684 --- 0.854927826 ---024 0.885007192 2012- 0.908748381 0.919844507 0.950705894 0.963623951 ---- 0.985367234 0.990089022 - 0.992048709 were 1.021948261 1.030049953 --- 1.04121672 1.075889666 1.084292129 1.094060033 1.110301526 1.117855941 4424052504 1.134053624 462760395 1.152769396 42020221 1.148383344 1.150323572 1.167654052 4475470955 1.175470855 4485707871 1.186707321 1.195299523 1.201338039 1.220019068 1.226483176 1.240571029 0.00E+00 3.60E+03 7.21E+03 1.08E+04 1.44E+04 ----- 1.80E+04 u 2.16E+04 - 2.52E+04 2.88E+04 3.24E+04 3.60E+04 3.96E+04 4.32E+04 4.68E+04 5.04E+04 5.41E+04 5.77E+04 6.13E+04 - 6.49E+04 Core 6.85E+04 7.21E+04 7.57E+04 7.93E+04 8.29E+04 8.65E+04 9.01E+04 9.37E+04 9.73E+04 1015 DE 1.01E+05 E.OF 1.04E+05 ROC. 1.08E+05 4400.5 1.12E+05 4.5 1.15E+05 106.5 1.19E+05 1 23.05 1.23E+05 1 1.26E+05 1.30E+05 1.33E+05 1.37E+05 1.41E+05 1.716188788 2.429322546 3.039344304 3.598553157 4.149165087 *** 4.678909656 0202020 5.1469627251 ---- 5.631900217 6.079092813 6.5085210871 6.894865984 7.284170697 e 7.641051562 02 7.974164702 8.288452099 8.602549154 8.888405272 9.145886112 . 9.382541608 e 9.608660196 120 9.822048725 --- 10.03992325 --- 10.24007043 10.42639657 10.59995116 10.76450427 1000AE0505 10.90450595 11 05276200 11.05776202 1117706511 11.17796511 44 27541237 11.32541337 112008763 11.42908763 11.54683266 11.65681381 11.76251444 11 86372097 11.86372987 11.93784994 12.02573653 12.1066495 12.18368066 7127405 7156405 How to perform nonlinear regression accross multiple datasets using MATLAB? I am trying to fit a model to mutiple data sets at once using non linear regression (by writing a suitable MATLAB programme. The model contains 3 unkown paramaters that must be tuned to satsifty (or give best model fit) accross 4 data sets at once. However, the model also con- tains 1 known paramater which is different for each of the 4 datasets. Model to fit: ARON Ron = + A2 In (1+ . ARon/Ron are the data set y values t is the data set x values A1, A2, y are unkown paramaters (common to all data sets) which must be found tau is a kown paramaer whcih differs accross all data sets . www wwwwwwwwwwwwwwwwwwwwwww Relavent information/data: tau = (3.10755e-19, 3.36127e-19, 3.22524E-19, 3.15416E-19), for datasets 1, 2, 3 and 4 re- spectively A1, A2 & gamma are unkown fitting paramaters (common to all data sets) $tau = (3.10755e-19, 3.36127e-19, 3.22524E-19, 3.15416E-19), for datasets 1, 2, 3 and 4 respectively & Al, A2 & gamma are unkown fitting paramaters (common to all data sets) Dataset 1 Dataset 2 Dataset 3 Dataset 4 X ly X ly Y Y 0 0 0.4182269741 0.509283834 0.566780287 0.618508886 0.650175014 0.691427136 0.714707096 ***** 0.739713491 ... 0.7879871481 0.00E+00 3.60E+03 7.21E-03 1.08E+04 1.44E+04 1.80E+04 2.16E+04 406 2.52E+04 2.88E+04 3.24E+04 3.60E+04 3.96E+04 4.32E+04 VELU 4.68E+04 5.04E+04 5.41E+04 5.77E+04 un 6.13E+04 du 6.49E+04 *** 6.85E+04 7.21E+04 ca 7.57E+04 7.93E+04 8.29E+04 8.65E+04 9.01E+04 9.37E+04 9.73E+04 1.01E+05 1.05E+05 1 OEEOE 1.08E+05 1 ORE. 110.B 1.12E+05 115.B 1.15E+05 1.19E+05 19ELE 122. 1.23E+05 125. 1.26E+05 1.30E+05 30.05 1.33E+05 1.37E+05 1.41E+05 0 0.850149996 1.19401382 1.470331057 1.717810636 1.951372271 ---- 2.180596328 ---- 2.363314416 *** 2.57788858 20020 2.781997996 **222*120 2.983129546 3.135282349 *** 3.304981827 102" 3.498450329 3.660977645 3.826092999 ***** 3.97241754 4.131648404 ve 4.272628003 4.432203018 w 4.569917484 10 4.701389988 0104-10 4.848194103 ---- 4.979318686 5.112661028 --- 5.253893067 5.390520777 5.520699905 594527044 5.624577911 5 749299901 5.748288891 BA050745 5.84950746 072310701 5.974319491 6097844507 6.087844507 C2000FZ57 6.20895757 6.335073822 6.436503398 6.531236171 6.61988729 6.73626975 6.859356225 0.00E+00 3.60E+03 7.21E+03 1.08E+04 1.44E+04 1.80E+04 2.16E+04 2.52E+04 2.88E+04 3.24E+04 *** 3.60E+04 3.96E+04 4.32E+04 4.68E+04 5.04E+04 5.41E+04 5.77E+04 e 6.13E+04 6.49E+04 6.85E+04 7.21E+04 7.57E+04 7.93E+04 8.29E+04 8.65E+04 9.01E+04 9.37E+04 9.73E+04 101.5 1.01E+05 1.04E+05 1.08E+05 RESOS 1.12E+05 1.15E+05 1 195205 1.19E+05 1.23E+05 1.26E+05 1.30E+05 1.33E+05 1.37E+05 1.41E+05 1.12465595 1.346980933 1.516458238 1.688253375 1.810110851 1.940857975 --- 2.067912147 ---- 2.167128124 - 2.2614281041 re 2.3530563 2.436567903 2.51467621 2012 2.617319793 ---- 2.712319122 ---- 2.804267945 2.887144632) - 2.958025421 ---- 3.037489696 2020 3.11890248 ---- 3.223051539 3.256562863 3.326180135 3.364235794 3.44037934 3.513280566 3.577158241 3.657893056 3.746943763 3.817675937 258617017 3.868647917 3.950778165 3.9369875241 4.000045553 4.055346407 A138745074 4.133746074 4.200906938 4.243181048 4.328056347 4.40089188 0.00E+00 3.60E+03 7.21E+03 1.08E+04 1.44E+04 1.80E+04 2.16E+04 2.52E+04 2.88E+04 3.24E+04 3.60E+04 3.96E+04 4.32E+04 4.68E+04 5.04E+04 5.41E+04 va 5.77E+04 Surre 6.13E+04 6.49E+04 6.85E+04 7.21E+04 7.57E+04 7.93E+04 8.29E+04 8.65E+04 9.01E+04 9.37E+04 9.73E+04 1017-05 1.01E+05 1.04E+05 1.08E+05 1.12E+05 1.15E+05 1.19E+05 + 1.23E+05 1.26E+05 1.30E+05 + 1.33E+05 1.37E+05 1.41E+05 0.854599684 --- 0.854927826 ---024 0.885007192 2012- 0.908748381 0.919844507 0.950705894 0.963623951 ---- 0.985367234 0.990089022 - 0.992048709 were 1.021948261 1.030049953 --- 1.04121672 1.075889666 1.084292129 1.094060033 1.110301526 1.117855941 4424052504 1.134053624 462760395 1.152769396 42020221 1.148383344 1.150323572 1.167654052 4475470955 1.175470855 4485707871 1.186707321 1.195299523 1.201338039 1.220019068 1.226483176 1.240571029 0.00E+00 3.60E+03 7.21E+03 1.08E+04 1.44E+04 ----- 1.80E+04 u 2.16E+04 - 2.52E+04 2.88E+04 3.24E+04 3.60E+04 3.96E+04 4.32E+04 4.68E+04 5.04E+04 5.41E+04 5.77E+04 6.13E+04 - 6.49E+04 Core 6.85E+04 7.21E+04 7.57E+04 7.93E+04 8.29E+04 8.65E+04 9.01E+04 9.37E+04 9.73E+04 1015 DE 1.01E+05 E.OF 1.04E+05 ROC. 1.08E+05 4400.5 1.12E+05 4.5 1.15E+05 106.5 1.19E+05 1 23.05 1.23E+05 1 1.26E+05 1.30E+05 1.33E+05 1.37E+05 1.41E+05 1.716188788 2.429322546 3.039344304 3.598553157 4.149165087 *** 4.678909656 0202020 5.1469627251 ---- 5.631900217 6.079092813 6.5085210871 6.894865984 7.284170697 e 7.641051562 02 7.974164702 8.288452099 8.602549154 8.888405272 9.145886112 . 9.382541608 e 9.608660196 120 9.822048725 --- 10.03992325 --- 10.24007043 10.42639657 10.59995116 10.76450427 1000AE0505 10.90450595 11 05276200 11.05776202 1117706511 11.17796511 44 27541237 11.32541337 112008763 11.42908763 11.54683266 11.65681381 11.76251444 11 86372097 11.86372987 11.93784994 12.02573653 12.1066495 12.18368066 7127405 7156405
