- F/f: the evolution model for linear/non-linear cases
- H/h: the observation model for linear/non-linear cases
- q: evolution noise
- r: observation noise
- J: the order of Taylor series approximation
For the synthetic linear dataset, we set F and H to take the controllable canonical and inverse canonical forms, respectively. F and H could take dimensions of 2x2, 5x5 and 10x10, while the evolution noise q and observation noise r take a constant gap of 20 dB. You could find sample datasets under Simulations/Linear_canonical/...
You could find Lorenz Attractor(LA) datasets under Simulations/Lorenz_Attractor/data/...
Inside this folder, data_gen.pt includes one trajectory of length 6,000,000 of LA model with J=5 and . The other sub-folders include Discrete-Time datasets of LA model of different trajectory lengths T and with J=5.
python main_linear.py
To change the parameters for your dataset, go to Extended_data.py
python main_lor.py
To change the parameters for your dataset, go to Simulations/Lorenz_Attractor/parameters.py