多元非线性回归matlab

在MATLAB中，可以使用nlinfit函数进行多元非线性回归。nlinfit函数的基本语法如下：
beta = nlinfit(X, Y, modelfun, beta0)
其中，X是预测变量，Y是响应值，modelfun是指定的模型函数，beta0是参数的初始值。nlinfit函数还可以使用其他可选参数，如options来选择评估算法。此外，nlinfit函数还可以返回残差R、雅可比矩阵J、估计方差-协方差矩阵CovB、均方差MSE和误差模型拟合信息ErrorModelInfo。以下是一个示例：
X = 1 : 10;
Y = [0 4 8 17 29 34 54 62 80 99];
mymodel = inline('beta(1) * X .^ beta(2)', 'beta', 'X');
beta0 = [1, 2];
beta = nlinfit(X, Y, mymodel, beta0);
输出结果为：
beta = 1.203177103441870 1.913267782105321
R = -1.203177103441870 -0.531902426953979 -1.844423386925135 -0.069922248918235 2.839481428235956 -3.080132352538584 4.200106803860550 -2.295789744078874 -0.547303920433009 0.463452664946701
J = 1.0e 02 * 0.009999999999931 0 0.037666127981753 0.031412881732651 0.081820231121898 0.108152737129980 0.141873719717092 0.236641274909903 0.217428643531115 0.421041233755697 0.308185129687587 0.664393679618334 0.413903226770619 0.969072093923712 0.534383368398917 1.337009450489727 0.669455022058049 1.769827653002497 0.818969511416963 2.268918061872248
CovB = 0.036203997364694 -0.013926635931111 -0.013926635931111 0.005406465988118
MSE = 5.764136230306152
ErrorModelInfo = ErrorModel: 'constant' ErrorParameters: 2.400861560004273 ErrorVariance: @(x)mse*ones(size(x,1),1) MSE: 5.764136230306152 ScheffeSimPred: 3 WeightFunction: 0 FixedWeights: 0 RobustWeightFunction: 0<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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