[x, t] = bodyfat_dataset;bodyfatNet = feedforwardnet (10);bodyfatNet =火车(bodyfatNet x, t);y = bodyfatNet (x);

genFunction (bodyfatNet“bodyfatFcn”）;y2 = bodyfatFcn (x);精度= max(abs(y-y2)- w自由:libBodyfat- t链接:自由bodyfatFcn

genFunction (bodyfatNet“bodyfatFcn”，“MatrixOnly”，“是的”）;y3 = bodyfatFcn (x);x1Type = code .typeof(double(0)，[13 Inf]);%输入1的编码器类型codegenbodyfatFcn.m配置:墨西哥人- obodyfatCodeGenarg游戏{x1Type}y4 = bodyfatodeGen (x);accuracy4 = max (abs (y-y4))

[x, t] = maglev_dataset;maglevNet = narxnet (1:2, 1:2, 10);[X, Xi,人工智能,T] = preparets (maglevNet, X, {}, T);maglevNet =火车(maglevNet X, T, Xi, Ai);[y, xf,房颤]= maglevNet (X, Xi, Ai);

genFunction (maglevNet“maglevFcn”）;(y2、xf af) = maglevFcn (X, Xi, Ai);精度= max(abs(cell2mat(y)-cell2mat(y2))- w自由:libMaglev- t链接:自由maglevFcn

genFunction (maglevNet“maglevFcn”，“MatrixOnly”，“是的”）;: x1 = cell2mat (X (1));将每个输入转换为矩阵x2 = cell2mat (X (2:));ξ1 = cell2mat(ξ(1:));%将每个输入状态转换为矩阵ξ2)= cell2mat(ξ(2:));[y3, xf1 xf2] = maglevFcn (x1, x2)(ξ1,ξ2));accuracy3 = max(abs(cell2mat(y)-y3)) x1Type = code .typeof(double(0)，[1 Inf]);%输入1的编码器类型x2Type = code .typeof(double(0)，[1 Inf]);%编码输入类型2xi1Type = code .typeof(double(0)，[1 2]);输入1状态的编码器类型xi2Type = code .typeof(double(0)，[1 2]);输入2状态的编码器类型codegenmaglevFcn.m配置:墨西哥人- omaglevNetCodeGenarg游戏{x1Type x2Type xi1Type xi2Type}[y4, xf1 xf2] = maglevNetCodeGen (x1, x2)(ξ1,ξ2));dynamic_codegen_accuracy = max (abs (cell2mat (y) y4))