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Since the signi?cance of the states represented by the nodes of these transition graphs is arbitrary, we will allow ourselves to use any ?nite set (such as {A,B,C,D,E, F,G,H} or even pairs of the sort we used to label the LTk transition graphs) to represent them. The key characteristics of these graphs are the fact that the state set encodes everything that is signi?cant about the computation and the fact that there are ?nitely many of those states. For that reason, the corresponding automata are known as Finite State Automata (FSAs). These come in two main varieties, Deterministic Finite State Automata (DFAs) and Non-Deterministic Finite State Automata (NFAs). We will focus initially on the deterministic variety. When we are talking about ?nite state automata in general, without regard to whether they are deterministic or not, we will use the term FSA.
short application for MISD
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s-> AACD A-> aAb/e C->aC/a D-> aDa/bDb/e
1. Does above all''s properties can be used to prove a language regular? 2..which of the properties can be used to prove a language regular and which of these not? 3..Identify one
Prove xy+yz+ýz=xy+z
Generate 100 random numbers with the exponential distribution lambda=5.0.What is the probability that the largest of them is less than 1.0?
Our DFAs are required to have exactly one edge incident from each state for each input symbol so there is a unique next state for every current state and input symbol. Thus, the ne
For example, the question of whether a given regular language is positive (does not include the empty string) is algorithmically decidable. "Positiveness Problem". Note that
Design a turing machine to compute x + y (x,y > 0) with x an y in unary, seperated by a # (descrition and genereal idea is needed ... no need for all TM moves)
The fact that regular languages are closed under Boolean operations simpli?es the process of establishing regularity of languages; in essence we can augment the regular operations
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