ECEN 898/498: Computational Modeling and Simulation I: Discrete Systems
Wednesday and Fridays: 1:15-2:30 p.m.
Goal: To introduce the fundamental concepts of computational modeling and simulation of discrete systems and their applications, ranging from description and development of a model to simulation and analysis of their results using simulation tools. Modeling technciques, including Monte Carlo Simulation, Markov chain, calibration, varification and validation of a model, sensitivity analysis in the context of a design will be covered.
Pre-requisite: Probability and statistics (ELEC 305, MATH 380, or equivalent) and a course in high level programming language
Course outline:
o Introuduction to discrete systems
• Deffintion, environment, and components of a system
o Introuduction to discrete system modeling
• Modeling methods, type of models (mathematical, ANN, fuzzy) Abstract-, descriptive-, static/stochastic models
• Physical and conceptual models
o Review of basic probability and statistics concepts
• PMF, CDF, central limit theorem, laws of large numbers, expectation, variance, probability distribution functions
o Random numbers (RN)
• Generation of RN using linear congruential method; tests for randomness; generation of random variate
o Mathematical and statistical models
• Stochastic processes; estimation of mean, variance and correlation; confidence interval; standard error, and hypothesis testing
o Queuing models
• Characteristics of queuing systems; queuing notation; single and multiple server queues; steady state behavior of finite and infinite population queue models; networks of queues
o Analysis of simulation data
• Input modeling; data collection; sample mean; sample variance Parameter estimation, sensitivity analysis
o Monte Carlo simulation and applications
For more info and registration, please contact Dr. Vakilzadian (hvakilzadian@unl.edu)