Encyclopedia > Generalized nondeterministic finite state machine
In the theory of computation, a generalized nondeterministic finite state machine or generalized nondeterministic finite automaton (GNFA) is a NFA where each transition may be labeled with any regular expression. The GNFA reads blocks of symbols from the input which constitute a string as defined by the regular expression on the transition.
Formal definition
A GNFA can be defined as a 5-tuple, (S, Σ, T, s, a), consisting of
a finite set of states (S)
a finite set call the alphabet (Σ)
a transition function (T : (S -{a}) × (S - {s}) → R)
A DFA or NFA can easily be converted into a GNFA and then the GNFA can be easily converted into a regular expression by repeatedly collapsing parts of it to single edges until S = {s, a}. Similarly, GNFAs can be reduced to NFAs by changing regular expression operators into new edges until each edge is labelled with a regular expression matching a single string of length at most 1. NFAs, in turn, can be reduced to DFAs using the powerset construction. This shows that GNFAs recognize the same set of formal languages as DFAs and NFAs.
State diagrams are used to graphically represent finitestatemachines.
A classic form of a state diagram for a finitestatemachine is a directed graph where
For a deterministic finitestatemachine (DFA), nondeterministicfinitestatemachine (NFA), generalizednondeterministicfinitestatemachine (GNFA), or Moore machine, input is signified on each edge
Finitestatemachines are studied in automata theory, a subfield of theoretical computer science.
Nondeterministic automata are usually implemented by converting them to deterministic automata—in the worst case, the generated deterministic automaton is exponentially bigger than the nondeterministic automaton (although it can usually be substantially optimised).
The problem of optimizing an FSM (finding the machine with the least number of states that performs the same function) is decidable, unlike the same problem for more computationally powerful machines.