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searching for Transition function 127 found (130 total)

alternate case: transition function

Finite-state machine (4,528 words) [view diff] exact match in snippet view article find links to article

of S {\displaystyle S} ; δ {\displaystyle \delta } is the state-transition function: δ : S × Σ → S {\displaystyle \delta :S\times \Sigma \rightarrow

input symbols called the alphabet Σ {\displaystyle \Sigma } , a transition function δ {\displaystyle \delta }  : Q × Σ → P ( Q ) {\displaystyle Q\times

makes a transition (or jump) to another state, according to its transition function, which takes the previous state and current input symbol as its arguments

second probabilistic transition function. At each step, the Turing machine probabilistically applies either the transition function δ 1 {\displaystyle \delta

by the transition function. The model is usually referred to as the STAR(p) models proceeded by the letter describing the transition function (see below)

Jacobian determinant. When the Jacobian determinant is positive, the transition function is said to be orientation preserving. An oriented atlas on M is an

similar to a LFSR, but they perform operations with carries so their transition function is nonlinear. F-FCSR was one of the eight algorithms selected for

"vanishing at infinity" makes no sense. A Feller transition function is a probability transition function associated with a Feller semigroup. A Feller process

In probability theory and related fields, a stochastic (/stəˈkæstɪk/) or random process is a mathematical object usually defined as a family of random

rejects infinite inputs. Such a machine has a set of states and a transition function, which determines which state the machine should move to from its

of states Q a finite set of input symbols called the alphabet Σ a transition function δ : Q × Σ → Q an initial (or start) state q 0 ∈ Q {\displaystyle

and β < 1 {\displaystyle \beta <1} are shape parameters. Smooth transition function normalized to (−1,1): f ( x ) = { 2 1 + e − 2 m x 1 − x 2 − 1 , |

called the input alphabet, and a function T: Q × Σ → Q called the transition function. Associated with any semiautomaton is a monoid called the characteristic

notion of tangent vectors and then directional derivatives. If each transition function is a smooth map, then the atlas is called a smooth atlas, and the

TM are replaced by pure or mixed states in a Hilbert space; the transition function is replaced by a collection of unitary matrices that map the Hilbert

automaton; it includes the probability of a given transition into the transition function, turning it into a transition matrix. Thus, the probabilistic automaton

part r, each drawn from a finite set of possible values. Define a transition function that sets the left part of a cell to be the left part of its left

symbol from a finite alphabet, together with a uniform rule called a transition function for updating all cells simultaneously based on the values of neighboring

\Gamma ^{n}\times \{L,R\}\right)} is a partial function called the transition function. Sometimes also denoted as δ ( Q i , [ x 1 , x 2 . . . x n ] ) =

two grains that are stacked vertically on top of each other, the transition function of the automaton replaces it by a block in which the grains are side-by-side

{\displaystyle \,\delta :Q\times \Sigma \times \Gamma \rightarrow S} is the transition function, where S {\displaystyle \,S} are finite subsets of Q × ( ‡ Γ + )

{\displaystyle W} of the Jacobian determinant of the coordinate transition function or its absolute value. A tensor density with a single index is called

\Gamma ^{k}\times \{L,R\}^{k}} is a partial function called the transition function, where L is left shift, R is right shift. A k {\displaystyle k} -tape

performed for any given situation. A deterministic Turing machine has a transition function that, for a given state and symbol under the tape head, specifies

states of the automaton, Σ is the set of input symbols, δ is the transition function that takes a state q and an input symbol x to a new state δ(q,x)

states that a function from a shift space to itself represents the transition function of a one-dimensional cellular automaton if and only if it is continuous

_{\text{c}}\cup \delta _{\text{r}}\cup \delta _{\text{int}}} is the transition function, which is partitioned into three parts corresponding to call transitions

diffusion. One such diffusion model could conceivably consist of a transition function based on the average values of the neighbourhood of the cell. Many

computational process is said to be logically reversible if the transition function that maps old computational states to new ones is a one-to-one function;

{\displaystyle M} is an atlas for M {\displaystyle M} such that each transition function is a smooth map, and two smooth atlases for M {\displaystyle M} are

states and α {\displaystyle \alpha } is the state-transition function. Applying the state-transition function to a state may yield two possible results: either

result of the state transition function. The state transition function is identical for every particle method. The state transition function is defined as S

called the alphabet of A. δ: Q × Σ → Q is a function, called the transition function of A. q0 is an element of Q, called the initial state. F is a set

belong to four different 2 × 2 blocks of the previous partition. The transition function for Critters counts the number of live cells in a block, and if this

that are true in it. Transitions between states are modeled by a transition function, which is a function mapping states into new states that result from

transition functions g i j {\displaystyle g_{ij}} form a cocycle of transition function on M. The associated fibre bundle ξ = ( E , p , M , g ) = G [ ( g

Q\times \Gamma \times \{L,R\}} is a partial function called the transition function, where L is left shift, R is right shift. If δ {\displaystyle \delta

\,\delta :Q\times \Gamma \rightarrow Q\times \Gamma ^{*}} is the transition function. A machine configuration is an ordered pair of its state and queue

} A finite set called the output alphabet O {\displaystyle O} A transition function δ : S × Σ → S {\displaystyle \delta :S\times \Sigma \rightarrow S}

Q is the set of states, Σ is the set of input symbols, T is the transition function (mapping a state and an input symbol to a set of states), q0 is the

reversible and/or locally unitary, and have an easily determined global transition function from the rule for updating individual cells. Recent results show

include three distinct states: active, quiescent, and firing, and the transition function must be such that a cell that is quiescent and whose neighbors are

formed by applying the update rule simultaneously to every cell. The transition function of the automaton is the function that maps each configuration to

{\displaystyle \delta _{ext}:Q\times X\rightarrow S} is the external transition function which defines how an input event changes a state of the system, where

vertex in graph theory. The Dirac delta function in mathematics. The transition function in automata. Deflection in engineering mechanics. The force of interest

finite set of states (S); a finite set called the alphabet (Σ); a transition function (T : (S ∖ {\displaystyle \setminus } {a}) × (S ∖ {\displaystyle \setminus

chart, regardless of the smoothness of the chart itself, then the transition function from that coordinate chart to any harmonic coordinate chart will

choices of global trivialization, which differ from one another by a transition function, which can be written as a function   g : R 1 , 3 → G   . {\displaystyle

Baum–Welch algorithm will estimate the starting probabilities, the transition function, and the observation function of a hidden Markov model. One common

of accepting, or final, states δ {\displaystyle \delta \,} is a transition function, where δ : ( Q × ( Σ ∪ { ε } ) × Γ ) ⟶ P ( Q × Γ ∗ ) {\displaystyle

called the alphabet of A. δ: Q × Σ → Q is a function, called the transition function of A. Q0 is an element of Q, called the initial state. Acc is the

in terms of state-transition function, or rule-set. The neighborhood (a grouping function) is part of the state-transition function, and defines for any

(equivalent) formalization, where δ {\displaystyle \delta } is the transition function, mapping Q × ( Σ ∪ { ε } ) × Γ {\displaystyle Q\times (\Sigma \cup

→ Q {\displaystyle \delta :Q\times \Sigma \rightarrow Q} is the transition function of A {\displaystyle {\mathcal {A}}} . q 0 {\displaystyle q_{0}} is

R2. If two such neighborhoods Ui and Uj overlap then there is a transition function φij defined on φj(Uj), with the standard property ϕ i j ∘ ϕ j = ϕ

from the agent's current beliefs to predicted observations, and a "transition function", which maps from current beliefs to future beliefs in the next time-step

\rightarrow {\mathcal {P}}(Q\times \Gamma \times \{L,R\})} is called the transition function (L shifts the head left and R shifts the head right) q 0 ∈ Q {\displaystyle

{\displaystyle \psi } with overlapping domains U and V, there is a transition function ψ ϕ − 1 : ϕ ( U ∩ V ) → ψ ( U ∩ V ) {\displaystyle \psi \phi ^{-1}:\phi

Proximity is preserved by the transition functions: For any r>1, for any transition function f in F, for any input-vector x, and for any two state-vectors s1

Russia may both contain Moscow. Given two overlapping charts, a transition function can be defined which goes from an open ball in R n {\displaystyle

y)}{\partial (s,t)}}} denotes the determinant of the Jacobian of the transition function from ( s , t ) {\displaystyle (s,t)} to ( x , y ) {\displaystyle

along a circle linking x. Glue the total space back together with a transition function which is a map from the cut circle to a representation of G. The

} is the set of input symbols, δ {\displaystyle \delta } is the transition function (mapping a state and an input symbol to a set of states), δ ∗ {\displaystyle

Approximations to the Heaviside step function could be made through Smooth transition function like 1 ≤ m → ∞ {\displaystyle 1\leq m\to \infty } : f ( x ) = { 1

input. This is written mathematically as δ : Q × Σ → Q, so δ (the transition function) in the definition of the FA is given by both the pair of vertices

{0, 1}. The behavior of a Turing machine M is determined by its transition function. This function can be easily encoded as a string over the alphabet

finite set called the output alphabet Λ {\displaystyle \Lambda } a transition function T : S × Σ → S {\displaystyle T:S\times \Sigma \rightarrow S} mapping

is orientable as a real manifold. For complex charts f and g with transition function h = f(g−1(z)), h can be considered as a map from an open set of R2

k-1} of possible digits in base-k notation; δ : Q × Σk → Q is the transition function; q0 ∈ Q is the initial state; the output alphabet Δ is a finite set;

{\frac {-Y}{Z-1}}\right),\end{aligned}}} so that the coordinate transition function is inversion in the circle: φ 01 ( x , y ) = φ 0 − 1 ∘ φ 1 ( x ,

from a regular expression E, the obtained automaton A with the transition function Δ[clarification needed] respects the following properties: A has

{\displaystyle t_{ij}:U_{i}\cap U_{j}\to G} is a continuous map called a transition function. Two G-atlases are equivalent if their union is also a G-atlas. A

sequentially updating the location posteriors, given a map and a transition function P ( x t | x t − 1 ) {\displaystyle P(x_{t}|x_{t-1})} , P ( x t |

:Q\times \Gamma \rightarrow Q\times \Gamma \times \{L,R\}} is the transition function; s ∈ Q {\displaystyle s\in Q} is the start state; t ∈ Q {\displaystyle

reason that this is an "informal example" is because writing down the transition function T {\displaystyle T} is difficult, and, even if written down, it is

to the power set, and a directed edge describes the action of the transition function. A path from the node of all states to a singleton state shows the

\{U_{\alpha }|\alpha \in \Sigma \}} then completely describes the state transition function of the DFA. Let Q represent the set of possible states of the DFA

trivializing open sets such that for any two such sets U and V, the transition function g U V : U ∩ V → GL ⁡ ( k , R ) {\displaystyle g_{UV}:U\cap V\to \operatorname

charts or "patches", which together cover all of S3. Note that the transition function between these two charts on their overlap is given by v = 1 ‖ u ‖

tractable problem transducer transition (see finite-state machine) transition function (of a finite-state machine or Turing machine) transitive relation

deterministic Turing machine, except rather than following a single transition function (a set of rules for how to proceed at each step of the computation)

{\displaystyle S_{\text{N}}^{5},S_{\text{S}}^{5}} and looking at the transition function on their intersection, which is a copy of S4, so S N 5 ∩ S S 5 ≃

α , U β {\displaystyle U_{\alpha },U_{\beta }} with holomorphic transition function g α β {\displaystyle g_{\alpha \beta }} , if s = s i e i = s ~ j

alphabet is {0, 1}, with 0 serving as the blank symbol. The machine's transition function takes two inputs: the current non-Halt state, the symbol in the current

{\text{otherwise}},\end{cases}}} A proper example of a smooth transition function will be: w ( x ) = { 1 1 + e 2 x − 1 x 2 − x if  0 < x < 1 , 0 if 

\delta \colon Q\times \Sigma \times \{0,1\}^{Q}\to \{0,1\}} is the transition function. For each string w ∈ Σ ∗ {\displaystyle w\in \Sigma ^{*}} , we define

metrics. Given a Riemannian metric on a two-dimensional manifold, the transition function between isothermal coordinate charts, which is a map between open

{\displaystyle a_{i}\in A_{i}} , the state updates based on the transition function T ( s , a , s ′ ) {\displaystyle T(s,a,s')} (using the current state

states Q = { q0, q1, q2 }, the input alphabet Σ = { a, b }, the transition function δ with δ(q0,a)=q0,   δ(q0,b)=q1,   δ(q1,a)=q2,   δ(q1,b)=q1,   δ(q2

V_{2}} of C n {\displaystyle \mathbb {C} ^{n}} respectively, then the transition function ψ ∘ φ − 1 : φ ( U 1 ∩ U 2 ) → ψ ( U 1 ∩ U 2 ) {\displaystyle \psi

connections is reduced to classifying the transition functions. The transition function maps the strip to G, and the different ways of mapping a strip into

→ z, which form an atlas. For points covered by both charts the transition function is multiplicative inversion on an open neighborhood of the point

problem. Let the interest r follow a Markov process with probability transition function Q ( r , d μ r ) {\displaystyle Q(r,d\mu _{r})} where d μ r {\displaystyle

bundle to a spin bundle, but one needs to choose a Z2 lift of each transition function, which is a choice of sign. The lift does not exist when the product

{\displaystyle \nu } . An alternating timed-automaton contains a transition function, which associates to a 3-tuple ( ℓ , a , c ) {\displaystyle (\ell

declination in astronomy noncentrality measure in statistics The transition function in the formal definition of a finite automaton, pushdown automaton

the stencil. T : S l → S {\displaystyle T\colon S^{l}\to S} is the transition function which is used to determine a cell's new state, depending on its neighbors

FDS (Finite Dynamical System) operating over a finite field. Each transition function is an element within a polynomial ring defined over the finite field

triple is associated with the unique matrix which, viewed as a "transition function" over D0R between the trivial bundles over (X \ x)R and over DR,

× X → X {\displaystyle T\colon \Sigma \times X\to X} called the transition function. Semiautomata arise from deterministic automata by ignoring the initial

the same as an ordinary k-string Turing machine, except that the transition function δ is restricted so that the input tape can never be changed, and

successful; the automaton accepts via an empty stack. The states and the transition function are not explicitly given; they are specified (generated) using a

:Q\times (\Sigma \cup \{\varepsilon \}\cup Q)\to Q} is a partial transition function, ε {\displaystyle \varepsilon } being the empty symbol, κ : Q → K

the original POMDP, τ {\displaystyle \tau } is the belief state transition function, r : B × A → R {\displaystyle r:B\times A\to \mathbb {R} } is the

result is known as Borel's lemma, after Émile Borel. With the smooth transition function g as above, define h ( x ) = g ( 2 + x ) g ( 2 − x ) , x ∈ R . {\displaystyle

have multiple transitions defined in the same configuration. Its transition function is δ : Q × ( Σ ∪ { L , R } ) → 2 Q × { l e f t , r i g h t } {\displaystyle

in the section above. The sigma points are propagated through the transition function f. x j = f ( s j ) j = 0 , … , 2 L {\displaystyle \mathbf {x} _{j}=f\left(\mathbf

states, Σ is the input alphabet, Δ is the tape alphabet, δ is the transition function, q0 is the initial state, Qa and Qr are the sets of accepting and

only on the number of states that are both initial and final. The transition function can be given as a matrix Δ σ ∈ R Q × Q {\displaystyle \Delta _{\sigma

uncertainty about the dynamics of the environment (learning the transition function) and how best to achieve its goals (learning the reward function)

{\mathbb {Z} }^{n}} , for n ≥ 2 {\displaystyle n\geq 2} , where the transition function is piecewise linear (here, an arbitrary point, e.g., the origin,

{\displaystyle K_{1}} ; and δ {\displaystyle \delta } (called the transition function) is a mapping from K 1 × ( Σ 1 ∪ { ϵ } ) × g ( Γ ∗ ) {\displaystyle

nonterminal for every state of the corresponding DFA and encoding its transition function into the definitions of these nonterminals — A ← 'x' B / 'y' C is

Moore Machine): A set of States A set of Inputs A set of Outputs A transition function (Input × State → State) An output function (Input × State → Output)

{\displaystyle C_{0}=0} . M ( q , b ) {\displaystyle M(q,b)} is the transition function of the Turing machine, written as a function from a doublet (machine

is the starting vertex; it is the one that is incremented by the transition function. Next, the sandpile models in general use undirected edges, so that

data on its tape. At this level we do not give details of states or transition function." Formal description: "... the lowest, most detailed, level of description

if Σ, Q, and δ denote the input alphabet, the state set, and the transition function of an automaton A, respectively, then A is called k-reversible if:

state and x t {\displaystyle x_{t}} the action selected; a state transition function g t ( s t , x t ) {\displaystyle g_{t}(s_{t},x_{t})} that leads the

{\lambda } }}={\dot {\mathbf {x} }}(t)\quad } which generates the state transition function f ( x ( t ) , u ( t ) , t ) = x ˙ ( t ) {\displaystyle \,\mathbf

over the same open subset U α {\displaystyle U_{\alpha }} , the transition function is precisely a local gauge transformation g α α : U α → G {\displaystyle

∗ ; d i ∈ D {\displaystyle D\subseteq C^{*}\,;\;d_{i}\in D} The transition function α {\displaystyle \alpha } is defined as α : D × Σ ∗ → Σ × D {\displaystyle

Q {\displaystyle \delta :Q\times \Sigma \mapsto Q} is a partial "transition" function of automaton, such that δ ( q , σ ) {\displaystyle \delta (q,\sigma

transducer schema is made up of three parts: The schema A state transition function σ An output function ω Models of computation extending on relational

{\displaystyle \delta :S\times \Sigma \rightarrow S\times O^{*}} is a transition function φ : S f → O ∗ {\displaystyle \varphi :S_{f}\rightarrow O^{*}} is

bundle S b a {\displaystyle S_{ba}} (see above in this section) transition function phase factor Φ Q P {\displaystyle \Phi _{QP}} parallel displacement

of its domain, ensuring that there is an output for each input. transition function See action table. transitive closure The smallest transitive relation