Widrow-Hoff delta rule method with linear layer (adaline)

i want to use learnwb which Widrow-Hoff delta rule (also known as least mean square (LMS) algorithm) and updatesits weights and bias when a training sample is presented. this is my code x=401*113 y=1*113 net = linearlayer(0,0.001); net = configure(net,x,y); net.trainFcn = 'trainb'; net.trainParam.epochs = 788; net=train(net,x,y); outputs=net(x); e=output-y ee=e.^2 eee=sum(ee) rmse=sqrt(mean(eee)); zz= postreg (outputs , y) but my error is root mean square error is very high ? could you please give my some suggestion ? is it my training method correct ?

[Stephen23]

duplicate:

https://www.mathworks.com/matlabcentral/answers/358528-widrow-hoff-delta-rule-method-with-linear-layer-adaline