3 edition of **Distributed state-space generation of discrete-state stochastic models** found in the catalog.

Distributed state-space generation of discrete-state stochastic models

- 236 Want to read
- 25 Currently reading

Published
**1995**
by Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, National Technical Information Service, distributor in Hampton, VA, [Springfield, Va
.

Written in English

- Complex systems.,
- Computer programming.,
- Distributed processing.,
- Formalism.,
- Markov processes.,
- Petri nets.,
- State vectors.,
- Vector spaces.

**Edition Notes**

Other titles | Distributed state space generation of discrete state stochastic models. |

Statement | Gianfranco Ciardo, Joshua Gluckman, David Nicol. |

Series | ICASE report -- no. 95-75., NASA contractor report -- 198233., NASA contractor report -- NASA CR-198233. |

Contributions | Gluckman, Joshua., Nicol, David., Institute for Computer Applications in Science and Engineering. |

The Physical Object | |
---|---|

Format | Microform |

Pagination | 1 v. |

ID Numbers | |

Open Library | OL18085488M |

We study distributed state space generation on a cluster of workstations. It is explained why state space partitioning by a global hash function is problematic when states contain variables from. Choose a custom storage class package by selecting a signal object class that the target package defines. For example, to apply custom storage classes from the built-in package mpt, select you use an ERT-based code generation target with Embedded Coder ®, custom storage classes do not affect the generated code.. If the class that you want does not appear in the Data Types: double | single.

Automated generation and analysis of Markov reward models using Stochastic Reward Nets. In Meyer, C. and Plemmons, R. J., editors, Linear Algebra, Markov Chains, and Queueing Models,volume 48 of IMA Volumes in Mathematics and its Applications,pages –Cited by: 2. Automated Parallelization of Discrete State-Space Generation Article in Journal of Parallel and Distributed Computing 47(2) April with 32 Reads How we measure 'reads'.

this paper we give an overview and a comparison of two parallel algorithms for the state space generation in stochastic modeling on common classes of multiprocessors. Distributed state-space generation of discrete-state stochastic models. (). A ﬂexible tool integrating partial order, compositional, and on-the-ﬂy veriﬁcation methods.

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Distributed state space generator that may be linked to a number of existing system modeling tools. We discuss partitioning strategies in the context of Petri net models, and report on performance observed on a network of workstations, as well as on a distributed memory multi-computer.

Discrete-state models are a valuable tool in the representation, design, and analysis of computer and. Because of the vast amount of memory consumed, we investigate distributed algorithms for the generation of state space graphs.

The distributed construction allows us to take advantage of the. Because of the vast amount of memory consumed, we investigate distributed algorithms for the generation of state space graphs. The distributed construction allows us to take advantage of the combined memory readily available on a network of workstations.

The key technical problem is to find effective methods for on-the-fly partitioning, so that the state space is evenly distributed among.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): High-level formalisms such as stochastic Petri nets can be used to model complex systems.

Analysis of logical and numerical properties of these models often requires the generation and storage of the entire underlying state space.

This imposes practical limitations on the types of systems which can be modeled. Analysis of logical and numerical properties of these models often requires the generation and storage of the entire underlying state space. This imposes practical limitations on the types of systems that can be modeled.

Because of the vast amount of memory consumed, we investigate distributed algorithms for the generation of state space by: High-level formalisms such as stochastic Petri nets can be used to model complex systems.

Analysis of logical and numerical properties of these models of ten requires the generation and storage of the entire underlying state space. This imposes practical limitations on the types of systems which can be modeled.

Because of the vast amount of memory consumed, we investigate distributed. Analysis of logical and numerical properties of these models often requires the generation and storage of the entire underlying state space.

This imposes practical limitations on the types of systems which can be modeled. Because of the vast amount of memory consumed, we investigate distributed algorithms for the generation of state space graphs. Get this from a library.

Distributed state-space generation of discrete-state stochastic models. [Gianfranco Ciardo; Joshua Gluckman; David Nicol; Institute for Computer Applications in. Both the logic and the stochastic analysis of discrete-state systems are hindered by the combinatorial growth of the state space underlying a high-level model.

In this work, we consider two orthogonal approaches to cope with this “state-space explosion”.Cited by: Distributed state-space generation of discrete-state stochastic models.

Technical ReportICASE, NASA Langley Research Center, Hampton, VA, Google ScholarCited by: A Distributed Algorithm for GSPN Reachability Graph Generation D.

NicolDistributed state-space generation of discrete-state stochastic models. INFORMS J. Comput., 10 (), pp. Google Scholar. 13 CRAY T3D System Architecture Overview, Cray Research by: 7. Ciardo, J.

Gluckman, D. NicolDistributed state space generation of discrete-state stochastic models INFORMS Journal on Computing, 10 (1) (), pp.

Google ScholarCited by: 5. We study distributed state space generation on a cluster of workstations. It is explained why state space partitioning by a global hash function is problematic when states contain variables from unbounded domains, such as lists or other recursive datatypes.

Distributed state-space generation of discrete-state stochastic models. A Database Approach to Distributed State Space Generation Stefan Blom Institute of Computer Science University of Innsbruck, Austria [email protected] Bert Lisser Jaco van de Pol Michael Weber 1 Department of Software Engineering CWI, Amsterdam, The Netherlands {bertl,vdpol,weber}@ Abstract We study distributed state space generation on a cluster of by: An Introduction to Stochastic Modeling, Revised Edition provides information pertinent to the standard concepts and methods of stochastic modeling.

This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models. A Database Approach to Distributed State-Space Generation Stefan Blom. Bert Lisser, Jaco Van De Pol, Michael Weber, A Database Approach to Distributed State-Space Generation, Journal of Logic and Computation, Vol Issue 1, FebruaryDistributed state-space generation of discrete-state stochastic models., Cited by: Learn how State-Space representation of time-series may be used to model stochastic processes.

Through an example application, MathWorks engineers will show you how state-space models can be defined, calibrated, estimated, and used to forecast time-series data sets. We study distributed state-space generation on a cluster of workstations.

It is explained why state-space partitioning by a global hash function is problematic when states contain variables from unbounded domains, such as lists or other recursive data types. Our solution is to introduce a database which maintains a global numbering of state values.

The distributed-memory approach implements dynamic load balancing mechanisms in step (1) to guarantee an equal distribution of the state space onto the main memories of the clustered machines. The shared-memory algorithms are based on elaborated synchronization mechanisms which allow parallel read and write access to the global irregular data Cited by: 1.

The mathematical space of a stochastic process is called its state space. This mathematical space can be defined using integers, real lines, -dimensional Euclidean spaces, complex planes, or more abstract mathematical spaces. The state space is defined using elements that reflect the different values that the stochastic process can take.

S. Allmaier, M. Kowarschik, and G. Horton. State Space Construction and Steady-State Solution of GSPNs on a Shared-Memory Multiprocessor. In Proceedings of the 7th IEEE International Workshop on Petri Nets and Performance Models PNPM’97 (Saint Malo, France), pages – IEEE CS-Press, Cited by: Second-Order Statistics Noise Model: Specifies a mathematical representation of the noise model of a stochastic state-space model.

You can create a noise model using the CD Construct Noise Model VI. This option is available only if you select Internal Noise from the Polymorphic instance pull-down menu. E{w}—Specifies the expected value or mean of the process noise vector.A stochastic or random process is a mapping from the sample space onto the real line.

Different types of stochastic processes are used in system modeling, and in this chapter some of these processes are discussed. These include stationary processes, counting processes, independent increment processes, Poisson processes, and martingales.