This paper studies the input design problem for system identification where time domain constraints have to be considered. A finite Markov chain is used to model the input of the s...
Non-trivial linear straight-line programs over the Galois field of two elements occur frequently in applications such as encryption or high-performance computing. Finding the shor...
In this study we deal with the mixing problem, which concerns combining the prediction of independently trained local models to form a global prediction. We deal with it from the ...
The wavelet decomposition is a proven tool for constructing concise synopses of massive data sets and rapid changing data streams, which can be used to obtain fast approximate, wit...
Reinforcement Learning (RL) is a simulation-based technique useful in solving Markov decision processes if their transition probabilities are not easily obtainable or if the probl...