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In the proposed approach, the discrete decision making process is approximated by continuous dynamics.
A nonlinear model of the process is approximated by a continuous-time external linear model.
Given a discretization method, a continuous random process is approximated by a finite set of random variables.
The nonlinear process is approximated by a multi-model consisting of the intelligent combination of some linear sub-models.
To meet the goal, the process is approximated using numerical methods, and then, the problem is posed like solving a system of linear equations.
As an example, a diffusion process is approximated by a state space discrete time model with n compartments in the spatial plane and is then reparametrized.
Similar(42)
The optimal time for flow rate adjustment during the process was approximated.
The spatial process was approximated from a subset of locations.
The two processes are approximated as independent of each other.
Such processes are approximated by collections of time dependent non-linear autoregressive models represented by a special kind of neuro-fuzzy neural network.
Through the dynamic simulation, the system open loop response has been obtained and the process dynamic is approximated by a simple model.
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