Thank you for sharing this great lecture! I find a bit confusing that you use the NRMSE in the sense of "fit", i.e. 100% when a model perfectly fits data. From my experience and according to the Wikipedia (NRMSD), the NRMSE is the RMSE normalized by the mean value or range, i.e. NRMSE=0% when a model perfectly fits data.
hi, where is the process of optimizing iteration? Seems you trained the prediction model by using RNN, but in the following, I didn't see the explicit control(search for optimal u(t)) process. Could you be brief to the point? thank you.
Found this very useful. Thank you for sharing the lecture.
Thank you for sharing this great lecture! I find a bit confusing that you use the NRMSE in the sense of "fit", i.e. 100% when a model perfectly fits data. From my experience and according to the Wikipedia (NRMSD), the NRMSE is the RMSE normalized by the mean value or range, i.e. NRMSE=0% when a model perfectly fits data.
hi, where is the process of optimizing iteration? Seems you trained the prediction model by using RNN, but in the following, I didn't see the explicit control(search for optimal u(t)) process. Could you be brief to the point? thank you.
Amend for my question, in slide TRAINING RNNS BY SEQUENTIAL LEAST-SQUARES you bring controller in, is that controller merged in RNN training process?
Very Informative, Thank you !
Thank u! Very interesting
Many thanks
Pᵣₒmₒˢᵐ