'Discrete state' presentation slideshows

System Modelling Models of Concurrency and their Applications April 2001 Axel Jantsch

System Modelling Models of Concurrency and their Applications April 2001 Axel Jantsch. Royal Institute of Technology. The Input-Output Modelling Process. Input variables: { u 1 (t), ..., u p (t)} t 0 ? t ? t f Output variables: { y 1 (t), ..., y m (t)} t 0 ? t ? t f

1k views • 100 slides

Stochastic Process

Stochastic Process. Formal definition A Stochastic Process is a family of random variables {X(t) | t ? T } defined on a probability space, indexed by the parameter t, where t varies over an index set T You are supposed to associated T with time Examples

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An Incremental Sampling-based Algorithm for Stochastic Optimal Control

An Incremental Sampling-based Algorithm for Stochastic Optimal Control. Martha Witick Department of Computer Science Rice University Vu Anh Huynh, Sertac Karamanm Emilio Frazzoli. ICRA 2012. Motion Planning!. Continuous time Continuous state space Continuous controls Noisy. MDPs?. S.

878 views • 72 slides

Modeling Sequential Processes

Modeling Sequential Processes. Simple Sequential Processes. Sequences of Events: State Dynamics Sequences of Responses Sequences of Decisions. Time Series Data. 700. Composite Index. 650. AMPLITUDE. 600. 550. 0. 50. 100. 150. 200. TIME. 600. Utility Index. 550. AMPLITUDE.

631 views • 62 slides

Models and methods in systems biology

Models and methods in systems biology . Daniel Kluesing Algorithms in Biology Spring 2009. http://www.estudisbarbera.es/hardware/articles/cpu/imatges/big/2000_Pentium4.jpg. Engineering Principles. Simple primitives Abstraction layers Composable Systems Robust and well characterized

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Introduction to Simulation

Introduction to Simulation. Overview. Simulation: Key Questions Introduction to Simulation Common Mistakes in Simulation Other Causes of Simulation Analysis Failure Checklist for Simulations Terminology Types of Models. Simulation: Key Questions.

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Introduction to Simulation

Introduction to Simulation. Andy Wang CIS 5930-03 Computer Systems Performance Analysis. Simulations. Useful when the system is not available Good for exploring a large parameter space However, simulations often fail Need both statistical and programming skills Can take a long time.

579 views • 37 slides

Introduction to Simulation

Introduction to Simulation. Andy Wang CIS 5930-03 Computer Systems Performance Analysis. Simulations. Useful when the system is not available Good for exploring a large parameter space However, simulations often fail Need both statistical and programming skills Can take a long time.

372 views • 37 slides

3442 Industrial Instruments 2 Chapter 8 Discrete-State Process Control

Princess Sumaya Univ. Electronic Engineering Dept. 3442 Industrial Instruments 2 Chapter 8 Discrete-State Process Control. Dr. Bassam Kahhaleh. 8: Discrete-State Process Control. Definitions The discrete state of the process at any moment is the set of all input and output values.

1.53k views • 32 slides

EE255/CPS226 Stochastic Processes

EE255/CPS226 Stochastic Processes. Dept. of Electrical & Computer engineering Duke University Email: bbm@ee.duke.edu , kst@ee.duke.edu. What is a stochastic process?. Stochastic Process: is a family of rv s { X(t)|t ? T } (T is an index set; it may be discrete or continuous)

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Non-Local Nuclear Dynamics

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Discretization Pieter Abbeel UC Berkeley EECS

Discretization Pieter Abbeel UC Berkeley EECS. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A A A A A A. Markov Decision Process. [Drawing from Sutton and Barto , Reinforcement Learning: An Introduction, 1998].

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Basic tandem model

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Markov Chain of DCF

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ECE 466/658: Performance Evaluation and Simulation

ECE 466/658: Performance Evaluation and Simulation. Introduction Instructor: Christos Panayiotou. Syllabus. Topics System Modeling and Modeling approaches Discrete Event Simulation Markov Chains Queueing Systems Queueing Networks Applications Additional Information

266 views • 15 slides

Arcade: A formal, extensible, model-based dependability evaluation framework

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Efficient Simulation Model for Hybrid Bond Graphs

Efficient Simulation Model for Hybrid Bond Graphs. Christopher Beers ISIS, Vanderbilt University. Introduction. Simulation of hybrid systems must combine two models of computation Hybrid Bond Graphs (HBGs) combine Continuous bond graph (BG) models with Switching junctions controlled by FSM

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where is the set of environmental stimuli and is the set of cellular responses.

and. A System-Theoretic Modeling Framework for Cellular Processes.

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where is the set of environmental stimuli and is the set of cellular responses.

and. A System-Theoretic Modeling Framework for Cellular Processes.

84 views • 1 slides