我有一个专家混合模型的代码，它在参数数量较少时可以正常工作。代码如下：
global x_au;global x_vi;global x_alpha;global y;global parameter;options = optimoptions(@fminunc,'GradObj', 'on', 'Algorithm','quasi-newton','MaxIter', 10000,'Display','iter-detailed'); % 更改迭代次数optTheta=[];x_au=x_au_train;x_vi=x_vi_train;x_alpha=x_alpha_train;y=y_train;parameter=zeros(8969,1);%期望步骤fprintf('opt1 begins');opt_1;fprintf('opt1 complete');%最大化步骤[x] = fminunc(@costfunction,parameter(1:4483),options);parameter(1:4483)=x;resnorm1=total_error(parameter(1:4483));k=1;count = 1;while(1)opt_1; fprintf('expectation complete');%最大化步骤[x] = fminunc(@costfunction,parameter(1:4483),options);parameter(1:4483)=x;resnorm2=total_error(parameter(1:4483));fprintf('resnorm1-resnorm2 - %f, resnorm2 - %f, k - %f',resnorm1-resnorm2,0.000001*resnorm2,k);if((resnorm1-resnorm2)< .000001*resnorm2 & k~=1) %% 为了减少训练时间 break;end但是现在，当我需要将它应用于参数数量较大的问题时，我得到了以下日志。
First-order Iteration Func-count f(x) Step-size optimality 0 1 5.31444e+10 4.75e+14Optimization stopped because the objective function cannot be decreased in the current search direction. Either the predicted change in the objective function,or the line search interval is less than eps. First-order Iteration Func-count f(x) Step-size optimality 0 1 5.31444e+10 4.75e+14Optimization stopped because the objective function cannot be decreased in the current search direction. Either the predicted change in the objective function,or the line search interval is less than eps.resnorm1-resnorm2 - 0.000000, resnorm2 - 53144.356560, k - 1.000000 First-order Iteration Func-count f(x) Step-size optimality 0 1 5.31444e+10 4.75e+14Optimization stopped because the objective function cannot be decreased in the current search direction. Either the predicted change in the objective function,or the line search interval is less than eps. resnorm1-resnorm2 - 0.000000, resnorm2 - 53144.356560, k - 2.000000>> 然后过程以非常糟糕的结果完成。可以看出，fminunc无法正确优化。谁能帮帮我？
回答：
我将参数初始化从零改为随机数，并结合归一化处理，最终使其正常工作。
