BackPropagation Through Time and arbitrary dynamic system

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BackPropagation Through Time (BPTT) is well established tool for training Recursive Neural Networks (RNN). The RNN (from my point of view) is a non-linear dynamic system, as it includes internal states and the activation functions are non-linear.



Therefore, BPTT should be viable option of training/adjusting parameters of any non-linear dynamic system. The motivation to change RNN for dynamic system, is that dynamic system may have desired physical representation (e.g. Equivalent Circuit in electronics) while RNN has hyper-parameters (usually not physically representable).



I have searched my university library and internet, but havent found any literature addressing the use of BPTT for other purpose than RNN training.



QUESTIONs:



1) Is there any problem, why BPTT is not used for fitting dynamic systems to measured data ?



2) If it is suitable, can you share any literature/examples/experience regarding the use of BPTT for training arbitrary (non-linear) dynamic system $fracdxdt=f_(x,y,t)$ with time-series data?




dynamical-systems nonlinear-optimization nonlinear-system neural-networks




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    BackPropagation Through Time (BPTT) is well established tool for training Recursive Neural Networks (RNN). The RNN (from my point of view) is a non-linear dynamic system, as it includes internal states and the activation functions are non-linear.



    Therefore, BPTT should be viable option of training/adjusting parameters of any non-linear dynamic system. The motivation to change RNN for dynamic system, is that dynamic system may have desired physical representation (e.g. Equivalent Circuit in electronics) while RNN has hyper-parameters (usually not physically representable).



    I have searched my university library and internet, but havent found any literature addressing the use of BPTT for other purpose than RNN training.



    QUESTIONs:



    1) Is there any problem, why BPTT is not used for fitting dynamic systems to measured data ?



    2) If it is suitable, can you share any literature/examples/experience regarding the use of BPTT for training arbitrary (non-linear) dynamic system $fracdxdt=f_(x,y,t)$ with time-series data?




    dynamical-systems nonlinear-optimization nonlinear-system neural-networks




    share|cite|improve this question





















      up vote
      0
      down vote

      favorite









      up vote
      0
      down vote

      favorite











      BackPropagation Through Time (BPTT) is well established tool for training Recursive Neural Networks (RNN). The RNN (from my point of view) is a non-linear dynamic system, as it includes internal states and the activation functions are non-linear.



      Therefore, BPTT should be viable option of training/adjusting parameters of any non-linear dynamic system. The motivation to change RNN for dynamic system, is that dynamic system may have desired physical representation (e.g. Equivalent Circuit in electronics) while RNN has hyper-parameters (usually not physically representable).



      I have searched my university library and internet, but havent found any literature addressing the use of BPTT for other purpose than RNN training.



      QUESTIONs:



      1) Is there any problem, why BPTT is not used for fitting dynamic systems to measured data ?



      2) If it is suitable, can you share any literature/examples/experience regarding the use of BPTT for training arbitrary (non-linear) dynamic system $fracdxdt=f_(x,y,t)$ with time-series data?




      dynamical-systems nonlinear-optimization nonlinear-system neural-networks




      share|cite|improve this question











      BackPropagation Through Time (BPTT) is well established tool for training Recursive Neural Networks (RNN). The RNN (from my point of view) is a non-linear dynamic system, as it includes internal states and the activation functions are non-linear.



      Therefore, BPTT should be viable option of training/adjusting parameters of any non-linear dynamic system. The motivation to change RNN for dynamic system, is that dynamic system may have desired physical representation (e.g. Equivalent Circuit in electronics) while RNN has hyper-parameters (usually not physically representable).



      I have searched my university library and internet, but havent found any literature addressing the use of BPTT for other purpose than RNN training.



      QUESTIONs:



      1) Is there any problem, why BPTT is not used for fitting dynamic systems to measured data ?



      2) If it is suitable, can you share any literature/examples/experience regarding the use of BPTT for training arbitrary (non-linear) dynamic system $fracdxdt=f_(x,y,t)$ with time-series data?





      dynamical-systems nonlinear-optimization nonlinear-system neural-networks





      share|cite|improve this question










      share|cite|improve this question




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      asked Aug 2 at 9:56









      Martin G

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