Inferring Extended Probabilistic Finite-State Automaton Models from Software Executions

2018 ◽  
Vol 27 (1) ◽  
pp. 1-39 ◽  
Author(s):  
S. S. Emam ◽  
J. Miller
Keyword(s):  
Finite State Automaton ◽  
Finite State

scholarly journals Combable functions, quasimorphisms, and the central limit theorem

2009 ◽  
Vol 30 (5) ◽  
pp. 1343-1369 ◽  
Author(s):  
DANNY CALEGARI ◽  
KOJI FUJIWARA
Keyword(s):  
Central Limit Theorem ◽  
Limit Theorem ◽  
Central Limit ◽  
Word Length ◽  
Hyperbolic Groups ◽  
Finite State Automaton ◽  
List Type ◽  
Generating Sets ◽  
Finite State ◽  
Word Hyperbolic Groups

AbstractA function on a discrete group is weakly combable if its discrete derivative with respect to a combing can be calculated by a finite-state automaton. A weakly combable function is bicombable if it is Lipschitz in both the left- and right-invariant word metrics. Examples of bicombable functions on word-hyperbolic groups include:(1)homomorphisms to ℤ;(2)word length with respect to a finite generating set;(3)most known explicit constructions of quasimorphisms (e.g. the Epstein–Fujiwara counting quasimorphisms).We show that bicombable functions on word-hyperbolic groups satisfy acentral limit theorem: if$\overline {\phi }_n$is the value of ϕ on a random element of word lengthn(in a certain sense), there areEandσfor which there is convergence in the sense of distribution$n^{-1/2}(\overline {\phi }_n - nE) \to N(0,\sigma )$, whereN(0,σ) denotes the normal distribution with standard deviationσ. As a corollary, we show that ifS1andS2are any two finite generating sets forG, there is an algebraic numberλ1,2depending onS1andS2such that almost every word of lengthnin theS1metric has word lengthn⋅λ1,2in theS2metric, with error of size$O(\sqrt {n})$.

scholarly journals Model Checking Temporal Logic Formulas Using Sticker Automata

2017 ◽  
Vol 2017 ◽  
pp. 1-33 ◽  
Author(s):  
Weijun Zhu ◽  
Changwei Feng ◽  
Huanmei Wu
Keyword(s):  
Model Checking ◽  
Temporal Logic ◽  
Complex Problem ◽  
Finite State Automaton ◽  
Dna Molecules ◽  
Single Stranded Dna ◽  
Finite State ◽  
Input Strings ◽  
Logic Model Checking ◽  
The Given

As an important complex problem, the temporal logic model checking problem is still far from being fully resolved under the circumstance of DNA computing, especially Computation Tree Logic (CTL), Interval Temporal Logic (ITL), and Projection Temporal Logic (PTL), because there is still a lack of approaches for DNA model checking. To address this challenge, a model checking method is proposed for checking the basic formulas in the above three temporal logic types with DNA molecules. First, one-type single-stranded DNA molecules are employed to encode the Finite State Automaton (FSA) model of the given basic formula so that a sticker automaton is obtained. On the other hand, other single-stranded DNA molecules are employed to encode the given system model so that the input strings of the sticker automaton are obtained. Next, a series of biochemical reactions are conducted between the above two types of single-stranded DNA molecules. It can then be decided whether the system satisfies the formula or not. As a result, we have developed a DNA-based approach for checking all the basic formulas of CTL, ITL, and PTL. The simulated results demonstrate the effectiveness of the new method.

scholarly journals A METHOD FOR INFERRING HIERARCHICAL DYNAMICS IN STOCHASTIC PROCESSES

2008 ◽  
Vol 11 (01) ◽  
pp. 1-16 ◽  
Author(s):  
OLOF GÖRNERUP ◽  
MARTIN NILSSON JACOBI
Keyword(s):  
Time Series ◽  
Stochastic Processes ◽  
A Priori ◽  
Main Idea ◽  
A Priori Knowledge ◽  
Finite State Automaton ◽  
State Spaces ◽  
Markovian Dynamics ◽  
Finite State ◽  
Operational Algorithm

Complex systems may often be characterized by their hierarchical dynamics. In this paper we present a method and an operational algorithm that automatically infer this property in a broad range of systems — discrete stochastic processes. The main idea is to systematically explore the set of projections from the state space of a process to smaller state spaces, and to determine which of the projections impose Markovian dynamics on the coarser level. These projections, which we call Markov projections, then constitute the hierarchical dynamics of the system. The algorithm operates on time series or other statistics, so a priori knowledge of the intrinsic workings of a system is not required in order to determine its hierarchical dynamics. We illustrate the method by applying it to two simple processes — a finite state automaton and an iterated map.

CONTROL OF COMPLEX PEAK-TO-PEAK DYNAMICS

2002 ◽  
Vol 12 (12) ◽  
pp. 2927-2936 ◽  
Author(s):  
CARLO PICCARDI ◽  
SERGIO RINALDI
Keyword(s):  
Hybrid Model ◽  
Chaotic Systems ◽  
Reduced Model ◽  
Finite State Automaton ◽  
Output Variable ◽  
One Dimensional ◽  
Chua's Circuit ◽  
Scalar Output ◽  
Finite State ◽  
And Control

The paper illustrates a method for the design of suitable controllers of chaotic systems characterized by complex peak-to-peak dynamics, namely by a recursive relationship between consecutive peaks (relative maxima) of a scalar output variable. For such systems, a reduced model can be defined which, in general, is a hybrid model composed of a one-dimensional map and a finite-state automaton. The issues related to the identification and control of such a reduced model are discussed with the help of three applications: the Chua's circuit, a market with advertizing, and a CO2laser.

Sign in / Sign up

Export Citation Format

Share Document