Petri net models of fuzzy neural networks

Ahson, S. I.

doi:10.1109/21.384255

Cited by 57 publications

(15 citation statements)

References 12 publications

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“…PN's can be combined with different techniques and theories such as fuzzy sets [10], neural networks (NN's) [11], [12] and object-oriented programming. These modified PN's are widely used in manufacturing systems [13], robots , knowledge based systems [14], and process control.…”

Section: Afhopn Proposed Modelmentioning

confidence: 99%

Modeling Intelligent ELearning Systems based on Adaptive Fuzzy Higher Order Petri Nets

Amin¹,

El-Ramly²,

Shebl³

2011

IJCA

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In this paper, we introduce adaptive and intelligent technologies in E-learning course development using Modular Object Oriented Dynamic Learning Environment (Moodle) based on Petri nets as a modeling formalism. Since classical Petri nets and fuzzy Petri nets are not adaptable according to the changes of the new incoming data such as the parameters of Moodle (static course material, interactive course material, activities), we introduce adaptive fuzzy higher order Petri net (AFHOPN) that is dynamically adjust the parameters. AFHOPN helps to describe and analyze the dynamic behavior, production inference of the intelligent E-learning systems and measure the learning rate.

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Section: Afhopn Proposed Modelmentioning

confidence: 99%

Modeling Intelligent ELearning Systems based on Adaptive Fuzzy Higher Order Petri Nets

Amin¹,

El-Ramly²,

Shebl³

2011

IJCA

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“…These differences are used as indexes for presenting the degree of AVS stenosis. The Fuzzy Petri net [20]- [23] is a dynamic and marked graphical system. This system has been extended to develop an inference algorithm to deal with Fuzzy reasoning problems and multi-criteria decisionmaking applications.…”

Section: Introductionmentioning

confidence: 99%

Arteriovenous Shunt Stenosis Evaluation Using a Fractional-Order Fuzzy Petri Net Based Screening System for Long-Term Hemodialysis Patients

Chen¹,

Kan²,

Lin³

2014

JBiSE

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This paper proposes the evaluation of arteriovenous shunt (AVS) stenosis using a fractional-order Fuzzy Petri net based screening system for long-term hemodialysis treatment of patients. The screening system uses the Burg method, the fractional-order chaos system, and the Fuzzy Petri net (FPN) for early detection of AVS dysfunction. The Burg method is an autoregressive (AR) model that is used to estimate the frequency spectra of a phonoangiographic signal and to identify the spectral peaks in the region from 25 Hz to 800 Hz. In AVS, the frequency spectrum varies between normal blood flow and turbulent flow. The power spectra demonstrate changes in frequency and amplitude as the degree of stenosis changes. A screening system combining fractional-order chaos system and FPN is used to track the differences in the frequency spectra between the normal and stenosis access. The dynamic errors are indexes that can be used to evaluate the degree of AVS stenosis using a FPN. For 42 long-term follow-up patients, testing results show that the proposed screening system is more efficient in the evaluation of AVS stenosis.

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“…In what follows, we give a brief description about the FPN modeling tool [10] [12]. Let consider FPN = (PN, CND, MF, FSR, FM).…”

Section: Fuzzy Petri Netsmentioning

confidence: 99%

“…This limitation of Petri nets has encouraged researchers to extend the exiting models by using the fuzzy reasoning theory [10] [11] [13]. The combination of Petri nets models and fuzzy theory has given rise to a new modeling tool called Fuzzy Petri Nets (FPN).…”

Section: Fuzzy Petri Netsmentioning

confidence: 99%

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A Fuzzy Petri Nets QoS Model for Wireless AD Hoc Networks

Khoukhi

Cherkaoui

Intelligence in Communication Systems

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Abstract:In this paper, we explore the use of Fuzzy Petri Nets for QoS support in wireless ad hoc networks. We propose a fiizzy Petri nets technique for modeling and analyzing the QoS decision making for traffic regulation. The proposed model, called FPWIM, studies the fuzzy regulation traffic rules in order to deal with the imprecise information caused by the dynamic topology of ad hoc networks. The input parameters of FPWIM are the node mobility and the delay measurement received by a node as feedback information from the MAC layer. The output parameter of FPWIM is the traffic regulation rate necessary to avoid the possible congestion in the network. Different traffic and network motilities are considered by FPWIM in order to help make an efficient QoS decision for various network conditions.

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Petri net models of fuzzy neural networks

Cited by 57 publications

References 12 publications

Modeling Intelligent ELearning Systems based on Adaptive Fuzzy Higher Order Petri Nets

Modeling Intelligent ELearning Systems based on Adaptive Fuzzy Higher Order Petri Nets

Arteriovenous Shunt Stenosis Evaluation Using a Fractional-Order Fuzzy Petri Net Based Screening System for Long-Term Hemodialysis Patients

A Fuzzy Petri Nets QoS Model for Wireless AD Hoc Networks

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