Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  investigate the relation beten two quantitative mea- sures characterizing short term memory in input driven dynamical systems, namely the short term memory capacity (MC) (2) and the Fisher memory curve (FMC) (1).  show that under some assumptions, the two quan- tities can be interpreted as squared 'Mahalanobis' norms of images of the input vector under the system's dynamics and that even though MC and FMC map the memory structure of the system from two quite different perspectives, they can be linked by a close relation. Input driven dynamical systems play an important role as machine learning models when data sets exhibit temporal dependencies, e.g. in prediction or control. In an attempt to characterize dynamic properties of such systems, measures have been suggested to quantify how ll past information can be represented in the system's internal state. In this contribution  investigate two such ll known measures, namely the short term memory capacity spectrum MCk (2) and the Fisher memory curve J(k) (1). The two quantities map the memory structure of the system under investigation from two quite different perspectives. So far their relation has not been closely investigated.   take the first step to bridge this gap and show that under some conditions MCk and J(k) can be related in an interpretable manner. 2 Background  study linear input driven state space models with N-dimensional state space and univariate inputs and outputs. Such systems can be represented e.g. by linear Echo State Networks (ESN) (3) with N recurrent (reservoir) units. The activations of the input, internal (state), and output units at time step t are de- noted by s(t), x(t), and y(t), respectively. The input-to-recurrent and recurrent- to-output unit connections are given by N-dimensional ight vectors v and u, respectively; connections beten the internal units are collected in an NN ight matrix W.  assume there are no feedback connections from the output to the reservoir and no direct connections from the input to the output. Under
['Peter Tino', 'Ali Rodan']
I am writing a research paper on a idea Short Term Memory Quantifications in Input-Driven Linear Dynamical Systems.
[{"from": "human", "value": "I am writing a research paper on a idea Short Term Memory Quantifications in Input-Driven Linear Dynamical Systems."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  investigate the relation beten two quantitative mea- sures characterizing short term memory in input driven dynamical systems, namely the short term memory capacity (MC) (2) and the Fisher memory curve (FMC) (1).  show that under some assumptions, the two quan- tities can be interpreted as squared 'Mahalanobis' norms of images of the input vector under the system's dynamics and that even though MC and FMC map the memory structure of the system from two quite different perspectives, they can be linked by a close relation. Input driven dynamical systems play an important role as machine learning models when data sets exhibit temporal dependencies, e.g. in prediction or control. In an attempt to characterize dynamic properties of such systems, measures have been suggested to quantify how ll past information can be represented in the system's internal state. In this contribution  investigate two such ll known measures, namely the short term memory capacity spectrum MCk (2) and the Fisher memory curve J(k) (1). The two quantities map the memory structure of the system under investigation from two quite different perspectives. So far their relation has not been closely investigated.   take the first step to bridge this gap and show that under some conditions MCk and J(k) can be related in an interpretable manner. 2 Background  study linear input driven state space models with N-dimensional state space and univariate inputs and outputs. Such systems can be represented e.g. by linear Echo State Networks (ESN) (3) with N recurrent (reservoir) units. The activations of the input, internal (state), and output units at time step t are de- noted by s(t), x(t), and y(t), respectively. The input-to-recurrent and recurrent- to-output unit connections are given by N-dimensional ight vectors v and u, respectively; connections beten the internal units are collected in an NN ight matrix W.  assume there are no feedback connections from the output to the reservoir and no direct connections from the input to the output. Under"}]