Discrete Time Stochastic Automata Network with Steady-State Product Form Distribution

2015 ◽  
pp. 241-250
Author(s):  
J. M. Fourneau
Keyword(s):  
Steady State ◽  
Discrete Time ◽  
Product Form ◽  
Stochastic Automata ◽  
Automata Network

scholarly journals Multipreconditioned GMRES for simulating stochastic automata networks

2018 ◽  
Vol 16 (1) ◽  
pp. 986-998
Author(s):  
Chun Wen ◽  
Ting-Zhu Huang ◽  
Xian-Ming Gu ◽  
Zhao-Li Shen ◽  
Hong-Fan Zhang ◽  
...  
Keyword(s):  
Steady State ◽  
Probability Distribution ◽  
Linear Systems ◽  
Iterative Methods ◽  
Communication Systems ◽  
Queueing Systems ◽  
Steady State Probability ◽  
Stochastic Automata ◽  
Automata Networks ◽  
Generator Matrices

AbstractStochastic Automata Networks (SANs) have a large amount of applications in modelling queueing systems and communication systems. To find the steady state probability distribution of the SANs, it often needs to solve linear systems which involve their generator matrices. However, some classical iterative methods such as the Jacobi and the Gauss-Seidel are inefficient due to the huge size of the generator matrices. In this paper, the multipreconditioned GMRES (MPGMRES) is considered by using two or more preconditioners simultaneously. Meanwhile, a selective version of the MPGMRES is presented to overcome the rapid increase of the storage requirements and make it practical. Numerical results on two models of SANs are reported to illustrate the effectiveness of these proposed methods.

Steady-state feedforward compensation for discrete time multivariable systems

1991 ◽  
Vol 1 (3) ◽  
pp. 253-276 ◽  
Author(s):  
H. A. Pak ◽  
Rowmau Shieh
Keyword(s):  
Steady State ◽  
Discrete Time ◽  
Multivariable Systems

PROCESSOR SHARING G-QUEUES WITH INERT CUSTOMERS AND CATASTROPHES: A MODEL FOR SERVER AGING AND REJUVENATION

2017 ◽  
Vol 31 (4) ◽  
pp. 420-435 ◽  
Author(s):  
J.-M. Fourneau ◽  
Y. Ait El Majhoub
Keyword(s):  
Steady State ◽  
Product Form ◽  
Processor Sharing ◽  
Service Capacity ◽  
Steady State Distribution ◽  
State Distribution ◽  
Signal Arrival ◽  
Open Networks ◽  
Networks Of Queues

We consider open networks of queues with Processor-Sharing discipline and signals. The signals deletes all the customers present in the queues and vanish instantaneously. The customers may be usual customers or inert customers. Inert customers do not receive service but the servers still try to share the service capacity between all the customers (inert or usual). Thus a part of the service capacity is wasted. We prove that such a model has a product-form steady-state distribution when the signal arrival rates are positive.

Discrete Time Voter Models: A Class of Stochastic Automata

1991 ◽  
pp. 83-94 ◽  
Author(s):  
R. W. R. Darling ◽  
Arunava Mukherjea
Keyword(s):  
Discrete Time ◽  
Stochastic Automata ◽  
Voter Models

Steady-state errors in discrete-time control systems

Automatica ◽  
1993 ◽  
Vol 29 (2) ◽  
pp. 523-526 ◽  
Author(s):  
Irwin W. Sandberg ◽  
Lilian Y. Xu
Keyword(s):  
Steady State ◽  
Control Systems ◽  
Discrete Time ◽  
Time Control

scholarly journals Iterative disaggregation for a class of lumpable discrete-time stochastic automata networks

2003 ◽  
Vol 53 (1) ◽  
pp. 43-69 ◽  
Author(s):  
Oleg Gusak ◽  
Tuğrul Dayar ◽  
Jean-Michel Fourneau
Keyword(s):  
Discrete Time ◽  
Stochastic Automata ◽  
Automata Networks
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