We describe the processes, assumptions, and limitations across four ITS modelling approaches, starting with the simplest and concluding with the most complex.
It is unknown whether encounters with cars to steal follow a Poisson process (Assumption 3).
However, if the equivalence of biological process assumption is denied, or simply doubted, one can argue for the opposing position.
A switching-type fuzzy neural networks (STFNNs) model is proposed which captures external disturbances, sensor nonlinearities, and mode switching phenomenon of the fuzzy neural networks without the Markovian process assumption.
It is considered that a production line where the probability of a transition will undergo from one state to another state depends only on the current state of the line (Markov process assumption).
The accuracy of the linear Cahn Hilliard Cook (CHC) theory in matrix form (multicomponent description) is scrutinized by comparing its exact macroscopic derivation with its phenomenological derivation based on the linear Markov process assumption.
Under the homogeneous Poisson process assumption, the distribution of the sample-POM, estimated from a finite sample of n bins, is asymptotically normal with zero mean and variance of 2/n.
We propose the concept of building thermal autonomy and a two-step process assumption-based design method.
All MC simulations are run on linear combiners and thus satisfy the driving process assumptions A1a and A1b.
The first step of the process assumption-based design method aims for the determination of building thermal qualities for a thermally autonomous building.
