Construction and Application of Learning Petri Net

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

doi:10.5772/48398

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…L * [12], ALERGIA [13], and their variants [74]- [80] are the mainly used algorithms for learning a model, especially for learning an automata model. Other learning algorithms, like learning PNs [81], [82] are not the focus of this paper.…”

Section: ) Learning For Verification By Learning a Modelmentioning

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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Section: ) Learning For Verification By Learning a Modelmentioning

confidence: 99%

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

et al. 2020

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“…Segundo [Belda 2009], a TV Digital vai além de "imagens limpas" e melhor qualidade de áudio, é uma eventual convergência da televisão, telefonia, internet e computador pessoal em um simples equipamento doméstico, com a expectativa de acesso amplo a todos os tipos de informação e comunicação interativa. A expressão tlearning descreve a união entre a TVDI (TV Digital Interativa) e o uso de tecnologia computacional para dar apoio ao treinamento e atividades educacionais [Feng et al 2012]. A união de elementos como televisão, computadores e das práticas do e-learning permitem aos usuários combinar a capacidade multimídia da TV com a interatividade e a personalização oferecidas pelo ambiente computacional, em especial a internet [Lytras et al 2002].…”

Section: T-learningunclassified

“…An approach for t-learning content generation based on a social media environment. [Feng et al 2012] Scirus Construction and Application of Learning Petri Net. [Baum and Soares 2012]…”

Section: Scirusmentioning

confidence: 99%

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Uma Contribuição ao Estabelecimento de Requisitos para Aplicações de Aprendizagem Baseada em TV-Digital

Costa¹,

Duarte²,

Filho³

et al. 2013

Simpósio Brasileiro De Informática Na Educação

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In recent years, the t-learning achieved great importance as a potential means of learning and training for apprentices, ensuring flexibility and convenience to teaching. This paper discusses the proposal of a preliminary set of characteristics and requirements for t-learning applications. A systematic review was conducted in order to understand how the scientific community investigates and describes these characteristics and requirements in the field of education.Resumo. Nos últimos anos, o t-learning atingiu uma grande importância como potencial meio de aprendizagem e treinamento para os aprendizes, garantindo flexibilidade e comodidade ao ensino. O presente trabalho discute a proposta de um conjunto preliminar de características e requisitos específicos para aplicações de t-learning. Uma revisão sistemática foi realizada com o intuito de compreender como a comunidade científica descreve e investiga tais características e requisitos neste domínio educacional.

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An Evolutionary Approach for Fuzzy Knowledge Learning

Barreto

2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Adaptive Fuzzy Petri Nets (AFPN) were proposed for knowledge reasoning and learning. They have advantage on learning dynamical knowledge, i.e., weights of an AFPN model are adjustable dynamically according to knowledge update. In this paper, an evolutionary algorithm called Adaptive Weights Evolutionary Algorithm (AWEA) is introduced which is capable of guaranteeing convergence of AFPN weights. Simulation results show effectiveness of AWEA. Comparing with the original back propagation learning algorithm of AFPN, AWEA does not depend on initial parameters to achieve convergence, so it avoids of getting trapped in local minimum. Additionally, AWEA converges faster than Backpropagation algorithms.

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Construction and Application of Learning Petri Net

Cited by 3 publications

References 23 publications

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

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

Uma Contribuição ao Estabelecimento de Requisitos para Aplicações de Aprendizagem Baseada em TV-Digital

An Evolutionary Approach for Fuzzy Knowledge Learning

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