0 个投票

function regresion_parametros_secador
clc, clear, clear all
TR =[0.422103409 0.42981426 0.437540563 0.445266867 0.45299317 0.460719473 0.468445777 0.47617208 0.483898384 0.491624687 0.499350991 0.507077294 0.514803597 0.522529901 0.530256204 0.537982508 0.545708811 0.553435115 0.561161418 0.568887721 0.576614025 0.592066632 0.607519238 0.622971845 0.638424452 0.653877059 0.669329666 0.684782273 0.70023488 0.715687486 0.731140093 0.7465927 0.762045307 0.777497914 0.792950521 0.808403128 0.823855734 0.839308341 0.854760948 0.870213555 0.885666162 0.901118769 0.916571376 0.932023982 0.947476589 0.962929196 0.978381803 0.99383441 0.99993819];
HFG =[2500.9 2489.1 2477.2 2465.3 2453.5 2441.7 2429.8 2417.9 2406.0 2394.0 2382.0 2369.8 2357.6 2345.4 2333.0 2320.6 2308.0 2295.3 2282.5 2269.5 2256.4 2229.7 2202.1 2173.7 2144.2 2113.7 2081.9 2048.8 2014.2 1977.9 1939.7 1899.7 1857.3 1812.7 1765.4 1715.1 1661.6 1604.4 1543.0 1476.7 1404.6 1325.7 1238.4 1140.1 1027.3 892.7 719.8 443.8 0.0];
modelfun = @(p,HFG) (exp((p(1)+p(2)*(log(1./TR))^0.1+p(3)/TR.^2+p(4)/TR.^3+p(5)/TR.^4)^0.5));
beta0 = [0 53.63746882 0.01601773 0.031311254 0.071298912];
mdl = fitnlm(TR,HFG,modelfun,beta0)
end
%ERROR MESSAGE%
Error using nlinfit (line 205)
Error evaluating model function
'@(p,HFG)(exp((p(1)+p(2)*(log(1./TR))^0.1+p(3)/TR.^2+p(4)/TR.^3+p(5)/TR.^4)^0.5))'.
Error in NonLinearModel/fitter (line 1123)
nlinfit(X,y,F,b0,opts,wtargs{:},errormodelargs{:});
Error in classreg.regr.FitObject/doFit (line 94)
model = fitter(model);
Error in NonLinearModel.fit (line 1430)
model = doFit(model);
Error in fitnlm (line 94)
model = NonLinearModel.fit(X,varargin{:});
Error in regresion_parametros_secador (line 14)
mdl = fitnlm(TR,HFG,modelfun,beta0)
Caused by:
Error using ^
One argument must be a square matrix and the other must be a scalar. Use POWER (.^) for elementwise
power.
>>

请先登录，再回答此问题。

 采纳的回答

Star Strider
Star Strider 2021-3-19

0 个投票

Use element-wise operations on every applicable operation in ‘modelfun’ and transpose ‘modelfun’ to return a column vector, and it works:
TR =[0.422103409 0.42981426 0.437540563 0.445266867 0.45299317 0.460719473 0.468445777 0.47617208 0.483898384 0.491624687 0.499350991 0.507077294 0.514803597 0.522529901 0.530256204 0.537982508 0.545708811 0.553435115 0.561161418 0.568887721 0.576614025 0.592066632 0.607519238 0.622971845 0.638424452 0.653877059 0.669329666 0.684782273 0.70023488 0.715687486 0.731140093 0.7465927 0.762045307 0.777497914 0.792950521 0.808403128 0.823855734 0.839308341 0.854760948 0.870213555 0.885666162 0.901118769 0.916571376 0.932023982 0.947476589 0.962929196 0.978381803 0.99383441 0.99993819];
HFG =[2500.9 2489.1 2477.2 2465.3 2453.5 2441.7 2429.8 2417.9 2406.0 2394.0 2382.0 2369.8 2357.6 2345.4 2333.0 2320.6 2308.0 2295.3 2282.5 2269.5 2256.4 2229.7 2202.1 2173.7 2144.2 2113.7 2081.9 2048.8 2014.2 1977.9 1939.7 1899.7 1857.3 1812.7 1765.4 1715.1 1661.6 1604.4 1543.0 1476.7 1404.6 1325.7 1238.4 1140.1 1027.3 892.7 719.8 443.8 0.0];
modelfun = @(p,HFG) (exp(sqrt(p(1)+p(2).*(log(1./TR)).^0.1+p(3)./TR.^2+p(4)./TR.^3+p(5)./TR.^4))).';
beta0 = [0 53.63746882 0.01601773 0.031311254 0.071298912];
mdl = fitnlm(TR,HFG,modelfun,beta0)
producing:
mdl =
Nonlinear regression model:
y ~ F(p,HFG)
Estimated Coefficients:
Estimate SE tStat pValue
________ _______ _______ __________
b1 -5.3764 0.53639 -10.023 6.2155e-13
b2 80.604 1.2895 62.51 1.2803e-44
b3 -12.018 1.5173 -7.9205 5.2486e-10
b4 6.8052 1.0612 6.413 8.3294e-08
b5 -1.1397 0.2096 -5.4375 2.2468e-06
Number of observations: 49, Error degrees of freedom: 44
Root Mean Squared Error: 12.4
R-Squared: 1, Adjusted R-Squared 1
F-statistic vs. zero model: 2.6e+05, p-value = 3.84e-97
.

更多回答（0 个）

类别

在 帮助中心 和 File Exchange 中查找有关 Regression 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by