Sentence examples for by a state vector from inspiring English sources

The distribution (class conditional distributions) to which the classifier is to be applied is parameterized by a state vector and the principle issue is to choose a design state that is optimal in comparison to all other states relative to some measure of robustness.

Now, assuming that both the quantum object and the measuring apparatus are quantum systems that each can be described by a wave function, it follows that their entangled state would likewise be represented by a state vector.

Each neuron i in population α is described by a state vector noted as X t i, N in R d and has an intrinsic dynamics governed by a drift function f α : R × R d ↦ R d and a diffusion matrix g α : R × R d ↦ R d × m assumed uniformly locally Lipschitz continuous with respect to the second variable.

We can describe these systems by a state vector x and a time coordinate t.

Time evolution of all species quantities are specified by a state vector X t) = [ X1 t), X2 t),..., X5 t)] T and state-change vector v μ (μ = 1, 2,... 8), corresponding to all reactions that describe the system.

In our model, each hand is conceptualized by a state vector that contains the x and y component in Euclidian coordinates of position (p), velocity (v), force (f), muscle activation (h), and target position (g).

By proposing a state vector for the STP induced by each stimulus, we show the distance of state vectors can be used to characterize learning process and several important phenomena of memory dynamics: partial memory recall, learning efficiency, learning with correlated stimuli.

Then, every target, say the ρth, is characterized by a 2K×1 state vector which contains both its position coordinates and velocity information for each coordinate.

Probabilistic model: Assume all relevant information of an unknown system at time instant n is captured by a latent (unobserved) state vector mathbf{z}_{n}=,[ z_{0,n},z_{1,n},ldots,z_{R-1,n}]^{mathrm{T}}.

It is achieved by introducing an auxiliary state vector: {varvec{y}} = { U_{i},K_{ij},L_{ij} } (4 where ( varvec{y} in R^{N + 2b} ) is the auxiliary state vector; N the number of buses; b the number of branches; U i  = v i 2 the square of voltage magnitude; and K ij  = v i v j  cos θ ij and L ij  = v i v j  sin θ ij the contribution of branch ij (from bus i to bus j) to y.

The model is developed by expanding the state vector as a mean state plus a fluctuating state.