Fuzzy automata system with application to target recognition based on image processing

Wu, QingE; Pang, Xuemin; Han, Zhenyu

doi:10.1016/j.camwa.2010.08.101

Cited by 13 publications

(5 citation statements)

References 11 publications

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“…Several fuzzy automata (FA), i.e., FDFA, FNFA, FFA and FIA had been introduced, as well as the equivalence among several FA was discussed in previous works [1] [2]. Thus, the partial relation of several FA is introduced as follows.…”

Section: Discussion Of Relation Of Several Fuzzy Automatamentioning

confidence: 99%

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Application of Union of Fuzzy Automata on Target Tracking

Wu¹,

Han²,

Wu³

2017

JILSA

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For better applications of fuzzy automata on target tracking, this paper presents an associated method of fuzzy automata by discussing the relation between fuzzy automata. The equivalence is mainly discussed regarding these fuzzy automata. The target tracking based on the associated method of fuzzy automata is given. Moreover, the simulation result shows that the associated method is better than single fuzzy automaton relatively. The development of these researches in this paper in turn can quicken the applications of the fuzzy automata in various fields.

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Section: Discussion Of Relation Of Several Fuzzy Automatamentioning

confidence: 99%

“…The extraction algorithm of various fuzzy automata had been discussed by using the neural networks in detail [1] [2]. To the extracted FA, its stability had been also described [1].…”

Section: Introductionmentioning

confidence: 99%

Application of Union of Fuzzy Automata on Target Tracking

Wu¹,

Han²,

Wu³

2017

JILSA

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“…Astrain et al [10] suggested how to measure the fuzziness between the strings. Wu et al [16] explained the fuzzy automata system to achieve improved image processing and target recognition. Using fuzzy finite automata system, firstly good image processing achieved and then carried out for the target recognition.…”

Section: Application Of Fuzzy Automatamentioning

confidence: 99%

A Novel Approach for the Determination of Membership Values of the Strings in Fuzzy Languages

Singh¹,

Kumar²

2014

IJCA

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Classical automata theory can not deal with uncertainty. To deal with uncertainty in system modeling, fuzzy automata come into the existence. Fuzzy automaton depends on membership value. For finding the membership degree of strings in fuzzy automata, the concept of maxmin automaton and minmax automaton can be used. Many researchers have used the concept of maxmin automaton for finding the fuzzy languages. In this paper, the concept of min max automaton for finding the membership degree of strings in fuzzy automata has been used.

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“…There are also applications of FA recently reported: Schmidt and Boutalis ( [4]) use fuzzy discrete event systems (FDES) in combination with multi-objective control for robot navigation; Wu, Pang and Han ( [5]) apply FA for target recognition based on image processing, and Bailador and Triviño use temporal fuzzy automata for pattern recognition ( [6]). …”

Section: Introductionmentioning

confidence: 98%

VHDL Framework for Modeling Fuzzy Automata

Todinca

Butoianu

2012

2012 14th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing

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Fuzzy set theory has been used to extend different domains of mathematics and also domains from applied sciences and from engineering, such that now there exists fuzzy logic, fuzzy arithmetic, fuzzy expert systems, fuzzy logic controllers (FLCs), fuzzy automata, fuzzy flip-flops, etc.In this work we concentrate on fuzzy automata (FA), which are fuzzy logic extensions of crisp automata (or finite state machines). Many researchers have noticed that, while crisp automata are widely used, being incorporated in almost any technical device, fuzzy automata have very few practical applications, being rather a theoretic concept. Among the factors that contributed to this situation we can enumerate: 1) the lack of controllability of FA (in certain conditions the degree of membership of the states of FA decrease towards zero and cannot be increased after that) and 2) there are too many types of fuzzy automata.We developed a VHDL framework that can be used for modeling fuzzy automata and for investigating their performance.In this paper we use several examples of FA from literature in order to illustrate how our VHDL framework can investigate the efficiency of different functions used for computing the degree of membership of the next state of a FA. We also show that some techniques proposed in literature for improving the performance of FA (more precisely, to avoid the degree of membership of FA states to decrease toward zero) are not effective on the fuzzy automata from these examples.

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Fuzzy automata system with application to target recognition based on image processing

Cited by 13 publications

References 11 publications

Application of Union of Fuzzy Automata on Target Tracking

Application of Union of Fuzzy Automata on Target Tracking

A Novel Approach for the Determination of Membership Values of the Strings in Fuzzy Languages

VHDL Framework for Modeling Fuzzy Automata

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