IDMEC Institute of Mechanical Engineering, Instituto Superior Técnico, University of Lisbon
Default: yada2 (2013-01-04)
Sandbox model and part of Default experiment
Description
(no description provided)
Measured data (61 lines)
X | Y | Z | Note |
---|---|---|---|
0.0 | 37.14 | 21.7829667335081 | 2.178296673350814274e+1 |
1.0 | 58.98 | 0.653723294235867 | 3.930654147577859020e+1 |
2.0 | 88.0 | 0.207831367145256 | 6.349030840803457345e+1 |
3.0 | 125.36 | 93.7285868951675 | 3.163141310483255058e+1 |
4.0 | 125.36 | 128.615095936528 | -3.255095936527582834e+0 |
5.0 | 143.77 | 166.312219741804 | -2.254221974180428460e+1 |
6.0 | 162.18 | 204.931123397104 | -4.275112339710396374e+1 |
7.0 | 199.0 | 242.814339670612 | -4.381433967061200914e+1 |
8.0 | 356.44 | 278.687056218857 | 7.775294378114338407e+1 |
9.0 | 316.0 | 311.694736187505 | 4.305263812494797033e+0 |
10.0 | 335.88 | 341.365541148167 | -5.485541148166753617e+0 |
11.0 | 377.82 | 367.535519185765 | 1.028448081423530450e+1 |
12.0 | 389.1525 | 390.264472403804 | -1.111972403804229237e+0 |
13.0 | 400.485 | 409.75896036595 | -9.273960365949714514e+0 |
14.0 | 423.15 | 426.309812178049 | -3.159812178049314691e+0 |
15.0 | 400.0 | 440.245679621667 | -4.024567962166692838e+1 |
16.0 | 400.0 | 451.90106509304 | -5.190106509303961862e+1 |
17.0 | 377.82 | 461.596022397634 | -8.377602239763428461e+1 |
18.0 | 377.82 | 469.624567165865 | -9.180456716586483688e+1 |
19.0 | 428.46 | 476.249188870771 | -4.778918887077138302e+1 |
20.0 | 479.11 | 481.69938967963 | -2.589389679630342972e+0 |
21.0 | 580.41 | 486.172708524751 | 9.423729147524862824e+1 |
22.0 | 580.41 | 489.837144364023 | 9.057285563597680584e+1 |
23.0 | 580.41 | 492.834249042911 | 8.757575095708872286e+1 |
24.0 | 671.9 | 495.282422963536 | 1.766175770364642322e+2 |
25.0 | 640.0 | 497.280131906044 | 1.427198680939563459e+2 |
26.0 | 625.1 | 498.908888777841 | 1.261911112221591211e+2 |
27.0 | 610.2 | 500.235925877881 | 1.099640741221189964e+2 |
28.0 | 580.41 | 501.316534426439 | 7.909346557356060643e+1 |
29.0 | 580.41 | 502.196078432902 | 7.821392156709804226e+1 |
30.0 | 580.41 | 502.911706551857 | 7.749829344814299066e+1 |
31.0 | 580.41 | 503.493793378237 | 7.691620662176321617e+1 |
32.0 | 471.89 | 503.967144036863 | -3.207714403686332601e+1 |
33.0 | 465.66 | 504.351995175059 | -3.869199517505897756e+1 |
34.0 | 459.44 | 504.664843021326 | -4.522484302132604030e+1 |
35.0 | 447.0 | 504.919125941675 | -5.791912594167487465e+1 |
36.0 | 400.0 | 505.125785461927 | -1.051257854619272456e+2 |
37.0 | 471.89 | 505.293726348726 | -3.340372634872629037e+1 |
38.0 | 471.89 | 505.430193223906 | -3.354019322390566087e+1 |
39.0 | 447.0 | 505.541078403193 | -5.854107840319269503e+1 |
40.0 | 441.0 | 505.631173222038 | -6.463117322203844432e+1 |
41.0 | 435.05 | 505.704373027959 | -7.065437302795890622e+1 |
42.0 | 423.15 | 505.763844252739 | -8.261384425273944186e+1 |
43.0 | 471.89 | 505.812160494446 | -3.392216049444639597e+1 |
44.0 | 447.0 | 505.851413301842 | -5.885141330184204865e+1 |
45.0 | 423.15 | 505.883302327261 | -8.273330232726144402e+1 |
46.0 | 524.28 | 505.909208665961 | 1.837079133403858572e+1 |
47.0 | 545.685 | 505.930254501691 | 3.975474549830849588e+1 |
48.0 | 567.09 | 505.947351604826 | 6.114264839517374524e+1 |
49.0 | 609.9 | 505.961240759507 | 1.039387592404931378e+2 |
50.0 | 497.6 | 505.972523811828 | -8.372523811828130881e+0 |
51.0 | 525.203333333333 | 505.981689717049 | 1.922164361628387704e+1 |
52.0 | 580.41 | 505.989135707509 | 7.442086429249089012e+1 |
53.0 | 609.9 | 505.995184493941 | 1.039048155060593457e+2 |
54.0 | 572.466666666667 | 506.000098242613 | 6.646656842405344644e+1 |
55.0 | 535.033333333333 | 506.004089932055 | 2.902924340127821695e+1 |
56.0 | 497.6 | 506.007332580255 | -8.407332580255197502e+0 |
57.0 | 377.82 | 506.009966741404 | -1.281899667414036096e+2 |
58.0 | 377.82 | 506.012106596536 | -1.281921065965362868e+2 |
59.0 | 377.82 | 506.013844901703 | -1.281938449017034343e+2 |
60.0 | 356.44 | 506.015257007895 | -1.495752570078946623e+2 |
Manual Regression Results
Manual regression was not calculated, click here to calculate now
Proxy Models (10)
Model | Params | RMSE | Note | ||
---|---|---|---|---|---|
LENP type Ib (ode) | n*=2.0e-14, M0=1.00099999999998, k11=2.0e-14, fr=0.08608599999998 | 457.85126 | goto | download .csv | |
Parabolic Regression | B=0.21723324570116, A=10.0 | 276.64245 | goto | download .csv | |
Live cell fraction model | r0=0.29442572174962, lambda=9.98712741219586, delta=0.49845109622855, alpha=8.44360217696903 | 74.98122 | goto | download .csv | |
Exponential | k=10.0, p=1.03420813277925, a=4.99999999999999 | 193.30336 | goto | download .csv | |
Linear Regression2 | Ordenada na origem=5.0, declive=5.0 | 304.50413 | goto | download .csv | |
Reversible Hills Equation | n=1.50915706391875, t=0.99999999999999, s=139.98216662442, KA=66.2959444152999, KI=10000.0, KmATP=25.0, KmPYR=23.2700010993108, KeqPK=7296.91754207207, KmADP=5.39934798653757, KmPEP=5.97300773867632, vmax_PK=2989.23222443587 | 150.37761 | goto | download .csv | |
Logistics | μ max.=0.22752335170313, λ=-1.04753119874789, A=2.58386405029478 | 73.94367 | goto | download .csv | |
Gompertz | μ max.=0.24926912376879, λ=-0.85929673465517, A=2.59243863588652 | 73.03057 | goto | download .csv | |
Richards | A=2.59244587378209, v=2.74649461e-06, μ max.=1.86110632e-06, λ=-0.85883084536446 | 73.03035 | goto | download .csv | |
Baranyi | m=0.971068178875, ymax=5.70502375454356, y0=3.72066129723362, v=0.92527863520883, μ max.=1.17794306038344, h0=20.0 | 71.22995 | goto | download .csv |