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2007-10-02
Stochastic Discrete Event Systems: Modeling, Evaluation, Applications - de Armin Zimmermann (Author)
Details Stochastic Discrete Event Systems: Modeling, Evaluation, Applications
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| Le Titre Du Fichier | Stochastic Discrete Event Systems: Modeling, Evaluation, Applications |
| Date de publication | 2007-10-02 |
| Traducteur | Riannan Gus |
| Chiffre de Pages | 941 Pages |
| La taille du fichier | 35.94 MB |
| Langage | Anglais & Français |
| Éditeur | Inanna Publications |
| ISBN-10 | 7938672280-OSH |
| Format de Données | AMZ EPub PDF DITA XPS |
| Créateur | Armin Zimmermann |
| ISBN-13 | 772-5606086903-SSG |
| Nom de Fichier | Stochastic-Discrete-Event-Systems-Modeling-Evaluation-Applications.pdf |
Télécharger Stochastic Discrete Event Systems: Modeling, Evaluation, Applications Livre PDF Gratuit
Stochastic Discrete Event Systems Delivers comprehensive overview on modeling with a quantitative evaluation of SDES This title presents important model classes like queuing networks Petri nets and automata
Introduction to stochastic processes used for modeling random systems and their most common applications In particular we In particular we study methods to compute the operating characteristics of such processes
Noté 005 Retrouvez Stochastic Discrete Event Systems Modeling Evaluation Applications et des millions de livres en stock sur Achetez neuf ou doccasion
The Maxplus project develops theory algorithms and applications of algebras of maxplus or tropical type in relation with the fields where these algebras arise decision theory deterministic and stochastic optimal control and game theory asymptotic analysis and probability theory modelling and performance analysis of discrete event dynamic systems transportation or telecommunication
TIMED MODELS OF DISCRETEEVENT SYSTEMS Timed State Automata Timed Petri Nets Stochastic Timed State Automata The Poisson counting process and Markov chain models INTRODUCTION TO DISCRETE EVENT MONTECARLO SIMULATION Basic concepts in discrete event simulation Model construction and applications Introduction to estimation theory MARKOV DECISION PROCESSES Dynamic Programming Solving
I have the following syllabus MODELING AND SIMULATION EEcT402E VIII SEMESTER UNIT 1 Introduction Systems Models and simulation concept of model model classification and mathematical representation Identification continuous and discrete static and dynamic deterministic and stochastic systems UNIT 2 Discrete event
Ainsi les résultats obtenus et les applications effectuées font des BDSPNs un outil de modélisation performant aussi bien pour lanalyse que pour la simulation