How to adjust trainNetwork to meet the solution of train?

I tested train and trainNetwork on a test bench problem to investigate the difference.

%%generate data
N = 1e3;
x = linspace(0,4*pi,N);
y = sin(x)+0.5*sin(3*x)+0.25*sin(7*x);

figure(1);
plot(x,y); hold on;

%%train network
net = feedforwardnet([10 10]);
net.layers{1}.transferFcn = 'logsig';   % or 'tansig'
net.layers{2}.transferFcn = 'logsig';
net.trainFcn = 'trainbr';               % or 'trainlm'
net = train(net,x,y);

%%predict
ypred = net(x);
plot(x,ypred,'.');

%%train network
inputSize = 1;
numResponses = 1;
numHiddenUnits = 10;

layers = [sequenceInputLayer(inputSize);
          fullyConnectedLayer(numHiddenUnits);
          fullyConnectedLayer(numHiddenUnits);
          fullyConnectedLayer(numResponses);
          regressionLayer];

opts = trainingOptions('adam','MaxEpochs',250,'Plots','training-progress','InitialLearnRate',0.01, ...
                       'LearnRateSchedule','piecewise','LearnRateDropFactor',0.75,'LearnRateDropPeriod',100, ...
                       'MiniBatchSize',9e9,'L2Regularization',0.0001);

%%train the network
net = trainNetwork(x,y,layers,opts);

%%predict
ypred = predict(net,x);

plot(x,ypred,'.');

I tried different options for trainNetwork but the solution (straight line) stays the same. How do I have to adjust the options in order to achive the same performance as train?