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JSim nested plots support modeling steps 6 8 above (verify accuracy, explore model state space, sensitivity analysis).
Full order model state space (n=8549), ROM state space (r=58), amount of inputs 34 and outputs 68.
Recall this assumption maintains that our model equations faithfully capture target system behavior and that the model state space faithfully represents the actual possibilities of the target system.
Suppose the nonlinear model state space is a faithful representation of the possibilities lying in the physical space of the target system.
No matter how fine-grained we make our model state space, it will still be the case that there are many different states of the actual target system (ontological states) that are mappable into the same state of the model state space (epistemic states).
For example, no matter how many observations of a system are made, there still will be a set of trajectories in the model state space that are indistinguishable from the actual trajectory of the target system.
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So it looks like fractal geometries in chaotic model state spaces bear no relationship to the pre-fractal features of actual-world systems.
The expansion of the sub-system 'Vehicle model (state-space)' is shown in Fig. 4.
Open image in new window Fig. 3 A general block diagram for simulation Open image in new window Fig. 4 Vehicle model (state-space) sub-system in SIMULINK.
The developed models include artificial neural network (ANN) model, transfer function model (TF), process model, state-space model and auto-regressive exogenous (ARX) model.
In the first step, the approach augments the model state-space with a set of P binary indicator variables δ = (δ1,..., δ P) and imposes a prior P on the regression coefficients that has expectation 0 and variance proportional to a P × P diagonal matrix with its entries equal to δ.
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