A learning Fuzzy Petri net model

Feng, Liangbing; Obayashi, Masanao; Kuremoto, Takashi; Kobayashi, Kunikazu

doi:10.1002/tee.21728

Cited by 15 publications

(7 citation statements)

References 12 publications

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“…First, the parameters in FPNs, such as weight, threshold, and certainty factor do not accurately represent increasingly complex knowledge-based expert systems and do not capture the dynamic nature of fuzzy knowledge [19]- [23]. Second, the fuzzy rules of most existing knowledge inference frameworks are static and cannot be adjusted dynamically according to variations of antecedent propositions [24]- [26]. In view of the complexity and the dynamic nature of knowledge-based systems, therefore, suitable models for knowledge-based systems should be adaptable [21].…”

Section: Related Workmentioning

confidence: 99%

Reliability Evaluation for Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net

Wang

Dai

et al. 2020

IEEE Access

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Due to failure, partial failure, or maintenance, the capacity of each machine is multi-state. Therefore, the limited relationship between the capacity of each machine and the input raw materials has to be considered. Additionally, in order to utilize the machine more effectively, the capacity of the buffers cannot be ignored, too. In this paper, a dynamic adaptive fuzzy reasoning Petri net is proposed to evaluate reliability of a manufacturing system with multiple production lines. Firstly, the model of manufacturing system is conducted, and from the perspective of demand, the minimum capacity vector and loading vector of each machine are determined. Secondly, knowledge representation and rules are formulated to establish weighted fuzzy petri nets. And the weighted fuzzy Petri net is adaptive based on the real-time level of buffers, the minimum capacity vector and loading vector. Moreover, the efficiency of product production can be improved while ensuring system reliability by adjusting the buffer level. Finally, a numerical experiment is used to demonstrate the application of our method.

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Section: Related Workmentioning

confidence: 99%

Reliability Evaluation for Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net

Wang

Dai

et al. 2020

IEEE Access

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“…PN and event calculus systems are true concurrency systems, labeled transition systems are false concurrency. In [119], a new learning algorithm that introduces the network learning method into PN update was proposed, and was used to model the web service discovery. In [120], a method based on random hill climbing that automatically builds PN models of non-linear (or multi-factorial) disease-causing gene-gene interactions was also described.…”

Section: H Fully Observation Outputs Vs Partially Observation Outputsmentioning

confidence: 99%

Survey on Learning-Based Formal Methods: Taxonomy, Applications and Possible Future Directions

et al. 2020

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Formal methods play an important role in testing and verifying software quality, especially in modern society with rapid technological updates. Learning-based techniques have been extensively applied to learn (a model or model-free) for formal verification and to learn system specifications, and resulted in numerous contributions. Due to the fact that adequate system models are often difficult to design manually and manual definition of specifications for such software systems gets infeasible, which motivate new research directions in learning models and/or specifications from observed system behaviors automatically. This paper mainly concentrates on learning-based techniques in formal methods area. An up-to-date overview of the current state-of-the-art in learning-based formal methods is provided in the paper. This paper is not a comprehensive survey of learning-based techniques in formal methods area, but rather as a survey of the taxonomy, applications and possible future directions in learning-based formal methods.

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“…Initially, the functionally similar services are extracted, and suitable WSs are selected. Consequently, the QoS of WS is predicted using the past QoS of data with the help of LFPN learning algorithm [19]. A location-aware Web service recommender system (LoRec) is an approach which predicts the QoS values of WSs according to the past QoS records to recommend the best services.…”

Section: Related Workmentioning

confidence: 99%

Towards an Effective QoS Prediction of Web Services using Context-Aware Dynamic Bayesian Network Model

2018

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The functionally equivalent web services (WSs) with different quality of service (QoS) leads to WS discovery models to identify the optimal WS. Due to the unpredictable network connections and user environment, the predicted values of the QoS are likely to fluctuate. The proposed Context-Aware Bayesian Network (CABN) system overcomes these limitations by incorporating the contextual factors in user, server, and environmental perspective. In this paper, three components are introduced for personalized QoS prediction. First, the CABN incorporates the pre-clustering model and reduces the searching space for QoS prediction. Second, the CABN confronts with the multi-constraint problem while considering the multi-dimensional QoS parameters of similar QoS data in WS discovery. Third, the CABN sends the normalized QoS value of records in similar as well as neighbor clusters as inputs to the Dynamic Bayesian Network and improves the prediction accuracy. The experimental results prove that the proposed CABN achieves better WS-Discovery than the existing work within a reasonable time.

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A learning Fuzzy Petri net model

Cited by 15 publications

References 12 publications

Reliability Evaluation for Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net

Reliability Evaluation for Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net

Survey on Learning-Based Formal Methods: Taxonomy, Applications and Possible Future Directions

Towards an Effective QoS Prediction of Web Services using Context-Aware Dynamic Bayesian Network Model

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