Pattern recognition using temporal fuzzy automata

Bailador, Gonzalo; Triviòo, Gracián

doi:10.1016/j.fss.2009.08.005

Cited by 47 publications

(34 citation statements)

References 22 publications

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“…The basic idea in the formulation is that, unlike the classical case, a fuzzy automaton can switch from one state to another one to a certain (truth) degree, and thus it is capable of capturing the uncertainty appearing in states or state transitions of a system. In the literature up to now (see, for example, [1], [8], [12], [14], [19], [23], [28], [34], [39], [41]), a great variety of types of fuzzy automata has been proposed in different modeling situations and the notion of fuzzy automata has proved useful in many areas such as learning control and pattern recognition. In parallel, various fuzzy Petri nets have been formulated and extensively investigated (see [4]- [6], [9], [18], [31] and the references therein).…”

Section: Introductionmentioning

confidence: 99%

Bisimulations for Fuzzy-Transition Systems

Cao

Chen

Kerre

2011

IEEE Trans. Fuzzy Syst.

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Abstract-There has been a long history of using fuzzy language equivalence to compare the behavior of fuzzy systems, but the comparison at this level is too coarse. Recently, a finer behavioral measure, bisimulation, has been introduced to fuzzy finite automata. However, the results obtained are applicable only to finite-state systems. In this paper, we consider bisimulation for general fuzzy systems which may be infinite-state or infiniteevent, by modeling them as fuzzy transition systems. To help understand and check bisimulation, we characterize it in three ways by enumerating whole transitions, comparing individual transitions, and using a monotonic function. In addition, we address composition operations, subsystems, quotients, and homomorphisms of fuzzy transition systems and discuss their properties connected with bisimulation. The results presented here are useful for comparing the behavior of general fuzzy systems. In particular, this makes it possible to relate an infinite fuzzy system to a finite one, which is easier to analyze, with the same behavior.

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Section: Introductionmentioning

confidence: 99%

Bisimulations for Fuzzy-Transition Systems

Cao

Chen

Kerre

2011

IEEE Trans. Fuzzy Syst.

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“…The use of fuzzy finite state systems is demonstrated in [19] for human gait modeling. Gonzalo Bailador and Graciá n Triviòo [20] propose a syntactic pattern recognition approach based on fuzzy automata, which can cope with the variability of patterns by defining imprecise models. The approach is called temporal fuzzy automata as it allows the inclusion of time restrictions to model the duration of the different states.…”

Section: Related Workmentioning

confidence: 99%

Activity Recognition with Multi-tapeFuzzy Finite Automata

Ali¹,

Amalarethinam²

2013

IJMECS

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-Recognizing the activities performed by the user in an unobtrusive manner is one of the important requisites of pervasive computing. Users perform a number of activities during their day to day life.Tracking and deciding what a user is doing at a given time involves a number of challenges. The lack of a precise pattern in doing an activity at different times is one among them. The number, order, and duration of the different steps involved in an activity vary significantly, even when the activity is done by the same user at different times. To overcome these challenges, a number of simultaneous inputs have to be handled with provisions for handling variations in number, order and duration of these inputs. This paper explains how multitape fuzzy finite state automata can be used to effectively recognize human activities. The method explained is found to give good results when tested using publicly available activity datasets collected in a smart home environment.

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“…An alternative would be to apply fuzzy techniques, such as adaptive fuzzy finite automaton. As reported by [1], models that rely on hidden states are difficult for human experts to understand, increasing complexity. In the opinion of [11] the capability of a more powerful class of grammars should rejuvenate syntactic research originally pursued in the 70s-80s.…”

Section: Final Considerationsmentioning

confidence: 99%

Adaptive Modeling of Digital Straightness Applied to Geometric Representation Enhancement

Neto¹,

Hirakawa²,

Massola³

2010

IJCA

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For representing of digitized straight line segments (DSLS), each of the available research techniques has its advantages and appropriate applications considering the complexities of real world scenarios. Based on adaptive finite automaton (AFA), we propose an alternative paradigm that is convenient for problems modeled by a set of rules. The main objective is to investigate the representation of DSLS through adaptivity, aiming to exploit the ability to represent tolerances, scalability, errors and deviations in angle or in length of the mentioned segments through a device called adaptive DSLS, for short ADSLS. Consequently, ADSLS is shown to be effective to represent segments; furthermore, it is able to adapt, reacting to circumstance stimuli in a single pass.

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Pattern recognition using temporal fuzzy automata

Cited by 47 publications

References 22 publications

Bisimulations for Fuzzy-Transition Systems

Bisimulations for Fuzzy-Transition Systems

Activity Recognition with Multi-tapeFuzzy Finite Automata

Adaptive Modeling of Digital Straightness Applied to Geometric Representation Enhancement

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