怎么用matlab求解Logistic模型中的三个参数?将Logistic模型的方程变为y=b/(1+a*exp(-kt)),已知t=[0,5,10,24,33,48,57,72,96,120,144,168,192,216];y=[0,0.028,0.103,0.336,0.450,0.597,0.716,0.778,0.835,0.849,0.816,0.839,0.811,0.816].怎么
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![怎么用matlab求解Logistic模型中的三个参数?将Logistic模型的方程变为y=b/(1+a*exp(-kt)),已知t=[0,5,10,24,33,48,57,72,96,120,144,168,192,216];y=[0,0.028,0.103,0.336,0.450,0.597,0.716,0.778,0.835,0.849,0.816,0.839,0.811,0.816].怎么](/uploads/image/z/4341355-43-5.jpg?t=%E6%80%8E%E4%B9%88%E7%94%A8matlab%E6%B1%82%E8%A7%A3Logistic%E6%A8%A1%E5%9E%8B%E4%B8%AD%E7%9A%84%E4%B8%89%E4%B8%AA%E5%8F%82%E6%95%B0%3F%E5%B0%86Logistic%E6%A8%A1%E5%9E%8B%E7%9A%84%E6%96%B9%E7%A8%8B%E5%8F%98%E4%B8%BAy%3Db%2F%281%2Ba%2Aexp%28-kt%29%29%2C%E5%B7%B2%E7%9F%A5t%3D%5B0%2C5%2C10%2C24%2C33%2C48%2C57%2C72%2C96%2C120%2C144%2C168%2C192%2C216%5D%3By%3D%5B0%2C0.028%2C0.103%2C0.336%2C0.450%2C0.597%2C0.716%2C0.778%2C0.835%2C0.849%2C0.816%2C0.839%2C0.811%2C0.816%5D.%E6%80%8E%E4%B9%88)
怎么用matlab求解Logistic模型中的三个参数?将Logistic模型的方程变为y=b/(1+a*exp(-kt)),已知t=[0,5,10,24,33,48,57,72,96,120,144,168,192,216];y=[0,0.028,0.103,0.336,0.450,0.597,0.716,0.778,0.835,0.849,0.816,0.839,0.811,0.816].怎么
怎么用matlab求解Logistic模型中的三个参数?
将Logistic模型的方程变为y=b/(1+a*exp(-kt)),已知t=[0,5,10,24,33,48,57,72,96,120,144,168,192,216];y=[0,0.028,0.103,0.336,0.450,0.597,0.716,0.778,0.835,0.849,0.816,0.839,0.811,0.816].怎么求出参数a,b,k的值,要具体的程序,
怎么用matlab求解Logistic模型中的三个参数?将Logistic模型的方程变为y=b/(1+a*exp(-kt)),已知t=[0,5,10,24,33,48,57,72,96,120,144,168,192,216];y=[0,0.028,0.103,0.336,0.450,0.597,0.716,0.778,0.835,0.849,0.816,0.839,0.811,0.816].怎么
建立m函数文件存为logistic1
function f=logistic1(b)
t=[0,5,10,24,33,48,57,72,96,120,144,168,192,216];y=[0,0.028,0.103,0.336,0.450,0.597,0.716,0.778,0.835,0.849,0.816,0.839,0.811,0.816];
f = y-b(1)./(1+b(2).*exp(-b(3).*t));
b0=[10,2,2];
>> b=leastsq('logistic1',b0)
b =
0.8221 13.9173 0.0818
或者cftool
General model:
f(x) = b/(1+a*exp(-k*x))
Coefficients (with 95% confidence bounds):
a = 13.92 (6.301,21.53)
b = 0.822 (0.7911,0.853)
k = 0.08184 (0.06479,0.0989)
Goodness of fit:
SSE:0.01404
R-square:0.9898
Adjusted R-square:0.9879
RMSE:0.03572