The phrase "a sequence of time steps" is correct and usable in written English.
It can be used in contexts related to processes, simulations, or analyses that involve discrete intervals of time.
Example: "In our experiment, we analyzed the data collected at each point in a sequence of time steps to observe the changes over the duration of the study."
Alternatives: "a series of time intervals" or "a progression of time steps".
The gates depicted in red can be omitted as they cancel out during a sequence of time steps.
T ={H 1, …, H t, H t + 1, …} denotes time, which is represented by a sequence of time steps with imperfect information for the PCS game process.
De Giorgi, in [36], defined what he called Generalized Minimizing Movements 2: A curve (x:[0,T]rightarrow X) is called Generalized Minimizing Movements (GMM) if there exists a sequence of time steps (tau _jrightarrow 0) such that the sequence of curves (x^{tau _j}) defined in (2.10) using the iterated solutions of (2.9) uniformly converges to x in [0, T].
We then update the reservoir over a sequence of time steps (we use six time steps, as in the original version (Nabieva et al., 2005)): R t a (u ) = R t − 1 a (u ) + ∑ v : (u, v ) ∈ E (g t a (v, u ) − g t a (u, v ) ), where g t a (v, u ) is the amount of flow a that moves from u to v at time t.
With a sequence of time step sizes (Delta t=0.1, 0.05, 0.025, 0.0125, 0.00625), we also compute the numerical errors at (T=5) for (p=2,4,6), respectively, and the results are presented in Table 1 in which a second-order in time accuracy is shown clearly.
Step 1: update primal variables Set T = T0, and select a sequence of time epochs {τ1, τ2,…} in continuous time.
CA will evolve in a sequence of discrete time steps.
It distinctly stands apart from multiple (local) time-stepping algorithms in that it requires neither selecting a global synchronization time step nor pre-determining a sequence of time-integration operations for individual parts of a heterogeneous numerical system.
Let T be the length of the observation sequence (i.e., number of time steps), O=[o 1,…,o T ] be the observation sequence, where each observation vector o t is characterized by p features (i.e., o t ∈ R p ), and Q=[q 1,…,q T ] be the state sequence.
Let T be the length of the observation sequence (i.e., number of time steps), let O={O 1,…,O T } be the observation sequence, and let Q={q 1,…,q T } be the state sequence.
We now know that every disease develops over time, through a sequence of defined biological steps, and that these steps may differ among individuals, based on genetic and environmental conditions.
