A fuzzy Petri net model for intelligent databases

Bostan-Korpeoglu, Burcin; Yazıcı, Adnan

doi:10.1016/j.datak.2006.08.003

Cited by 31 publications

(10 citation statements)

References 23 publications

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“…Bostan- Korpeoglu and Yazici (2007) propose a fuzzy Petri net model for intelligent databases to represent knowledge and the behavior of an intelligent objectoriented database environment, which integrates fuzzy, active and deductive rules with database objects. The techniques and solutions provided in this study can be used in various complex systems, such as weather forecasting applications, environmental information systems and defense applications.…”

Section: Integration Of Artificial Intelligent and Dbms Technologiesmentioning

confidence: 99%

Introduction to Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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This chapter presents definitions and descriptions of intelligent decision support systems (IDSSs) and analyzes the technology and AI methods, which serve as bases of the IDSS. Scholars have offered various definitions of IDSS. Every one of them accents that an intelligent decision support system is a DSS, which makes extensive use of artificial intelligence techniques. Artificial intelligence techniques can be utilized in all the components of IDSSs, such as in the data base, knowledge base, model base, user interface and the rest. Therefore this chapter deliberates the intelligent databases, hardware (sensors, iris camera hardware, hardware for fingerprint biometric identification, etc.) and computer human interfaces (gesture, intelligent user, motion tracking, voice and natural-language interfaces) in intelligent decision support systems.

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Section: Integration Of Artificial Intelligent and Dbms Technologiesmentioning

confidence: 99%

Introduction to Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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“…In order to put the effect of seasons, Table 2 illustrates the rules of expected weather according to the season (see Table 3). In [14], a fuzzy Petri net model is introduced to represent the behavior of intelligent object-oriented database environment for the weather forecasting application. The object-oriented fuzzy Petri net divide a complex knowledge into several function subsystems.…”

Section: Weather Forecasting Applicationmentioning

confidence: 99%

Reasoning dynamic fuzzy systems based on adaptive fuzzy higher order Petri nets

Amin

Shebl

2014

Information Sciences

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“…The advantages of using FPNs in fuzzy rule-based reasoning systems include (Chen et al, 1990;Bostan-Korpeoglu and Yazici, 2007): (1) the graphical representation of FPNs model can help to visualize the inference states and modify fuzzy rule bases; (2) the analytic capability, which can express the dynamic behavior of fuzzy rule-based reasoning. Evaluation of markings is used to simulate the dynamic behavior of the system.…”

Section: Introductionmentioning

confidence: 99%

“…Evaluation of markings is used to simulate the dynamic behavior of the system. The explanation of how to reach conclusions is expressed through the movements of tokens in FPNs (Bostan-Korpeoglu and Yazici, 2007). The field of fuzzy Petri nets may have an important impact in understanding how biological systems work, giving at the same time a way to describe, manipulate, and analyse them.…”

Section: Introductionmentioning

confidence: 99%

Confidence value prediction of DNA sequencing with Petri net model

Hamed

Ahson

2011

Journal of King Saud University - Computer and Information Scie

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In this paper, a fuzzy Petri net (FPN) approach to modeling fuzzy rule-based reasoning is proposed to determining confidence values for bases called in DNA sequencing. The proposed approach is to bring DNA bases-called within the framework of a powerful modeling tool FPN. The three input features in our fuzzy model-the height, the peakness, and the spacing of the first most likely candidate (the base called) and the peakness and height for the second likely candidate can be formulated as uncertain fuzzy tokens to determines the confidence values. The FPN components and functions are mapped from the different type of fuzzy operators of If-parts and Thenparts in fuzzy rules. The validation was achieved by comparing the results obtained with the FPN model and fuzzy logic using the MATLAB Toolbox; both methods have the same reasoning outcomes. Our experimental results suggest that the proposed models, can achieve the confidence values that matches, of available software.

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A fuzzy Petri net model for intelligent databases

Cited by 31 publications

References 23 publications

Introduction to Intelligent Decision Support Systems

Introduction to Intelligent Decision Support Systems

Reasoning dynamic fuzzy systems based on adaptive fuzzy higher order Petri nets

Confidence value prediction of DNA sequencing with Petri net model

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