matcont中的DATENUM failed的问题
各位大神，我在用matcont画微分方程的分岔图的时候，开始计算compute->forward的时候就出现DATENUM failed的问题，请问有大神知道是怎么回事，是什么意思吗？data:image/png;base64,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
最近做毕业设计也遇到类似问题，有大神能解释一下吗？ 请问有没有人知道这个问题怎么解决？ Cl_matcont是一个常微分方程（组）的MatLab分岔软件包，其中有个system文件夹，在这里可以定义方程或
方程组，但其中有几个矩阵如jacobian，jacobianp，hessian，hessiansp，der3,der4,der5，一般资料中没有给出怎样计算它们的方法，如果算不出这些矩阵就没法进行分岔分析模拟，现将这几个矩阵的算法注解如下，即%后的注解。 假设system中有个bratu文件，编程如下,则其中的矩阵，如jacobian，jacobianp，hessian，hessiansp，der3,der4,der5等矩阵可作如下注解。
function out = bratu
out{1} = @init;
out{2} = @fun_eval;
out{3} = @jacobian;
out{4} = @jacobianp;
out{5} = @hessians;
out{6} = @hessiansp;
out{7} = @der3;
out{8} = [];
out{9} = [];
out{10}= @userf1;% --------------------------------------------------------------------------
function dydt = fun_eval_r(t,kmrgd,a) %定义微分方程组
dydt=[-2*kmrgd(1)+kmrgd(2)+a*exp(kmrgd(1));;
kmrgd(1)-2*kmrgd(2)+a*exp(kmrgd(2));;];% --------------------------------------------------------------------------
function = init
y0=;%初始条件
options = odeset('Jacobian',handles(3),'JacobianP',handles(4),'Hessians',handles(5),'HessiansP',handles(6));
handles = feval_r(bratu);
tspan = ;%运行时间% --------------------------------------------------------------------------
function jac = jacobian(t,kmrgd,a)
jac=[ a*exp(kmrgd(1)) - 2 , 1 ; 1 , a*exp(kmrgd(2)) - 2 ]; %dydt关于kmrgd(1)和kmrgd(2)计算雅可比矩阵
% --------------------------------------------------------------------------
function jacp = jacobianp(t,kmrgd,a)
jacp=[ exp(kmrgd(1)) ; exp(kmrgd(2)) ]; %dydt关于参数a计算雅可比矩阵
% --------------------------------------------------------------------------
function hess = hessians(t,kmrgd,a)
hess1=[ a*exp(kmrgd(1)) , 0 ; 0 , 0 ];
hess2=[ 0 , 0 ; 0 , a*exp(kmrgd(2)) ];%dydt关于kmrgd(1)和kmrgd(2)计算hessian矩阵
hess(:,:,1) =hess1;
hess(:,:,2) =hess2;
% --------------------------------------------------------------------------
function hessp = hessiansp(t,kmrgd,a)
hessp1=[ exp(kmrgd(1)) , 0 ; 0 , exp(kmrgd(2)) ]; %雅可比矩阵关于参数a计算导数
hessp(:,:,1) =hessp1;
%---------------------------------------------------------------------------
function tens3= der3(t,kmrgd,a) %共有2*2个导数矩阵，即变量数为2，则为2*2个导数矩阵
tens31=[ a*exp(kmrgd(1)) , 0 ; 0 , 0 ]; %hessian矩阵hess1关于kmrgd(1)求导数
tens32=[ 0 , 0 ; 0 , 0 ];%hessian矩阵hess1关于kmrgd(2)求导数
tens33=[ 0 , 0 ; 0 , 0 ];%hessian矩阵hess2关于kmrgd(1)求导数
tens34=[ 0 , 0 ; 0 , a*exp(kmrgd(2)) ]; %hessian矩阵hess2关于kmrgd(2)求导数
tens3(:,:,1,1) =tens31;
tens3(:,:,1,2) =tens32;
tens3(:,:,2,1) =tens33;
tens3(:,:,2,2) =tens34;
%---------------------------------------------------------------------------
function tens4= der4(t,kmrgd,a) %对der3关于各变量再次求导，共2*2*2个矩阵
%---------------------------------------------------------------------------
function tens5= der5(t,kmrgd,a) %对der4关于各变量再次求导，共2*2*2*2个矩阵
function userfun1=userf1(t,kmrgd,a)
userfun1=a-0.2;
Posion 发表于 2016-5-17 16:29
Cl_matcont是一个常微分方程（组）的MatLab分岔软件包，其中有个system文件夹，在这里可以定义方程或
方 ...
请问如果我在遇到的这种问题是什么原因，应该怎么解决，刚开始学习，好乱
Posion 发表于 2016-5-17 16:44
就是这样设定的，第一次计算没有问题，重新算的时候就一直提示DATENUM failed zhuchunhua0128 发表于 2016-5-17 16:56
就是这样设定的，第一次计算没有问题，重新算的时候就一直提示DATENUM failed
是不是workspace中的变量有问题？
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