If you need to add another course to your spring schedule, consider enrolling in ECEN 898/498: Computational Modeling and Simulation I: Discrete Systems.
ECEN 898/498 (Spring 2021)
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.
Prerequisite: Probability and statistics (ELEC 305, MATH 380, or equivalent) and a course in high level programming language
Course outline:
Introduction to discrete systems:
—Definition, environment, and components of a system
Introduction to discrete system modeling:
—Modeling methods, type of models (mathematical, ANN, fuzzy)
—Abstract-, descriptive-, static/stochastic models
—Physical and conceptual models
Review of basic probability and statistics concepts:
—PMF, CDF, central limit theorem, laws of large numbers, expectation, variance, probability distribution functions
Random numbers (RN):
—Generation of RN using linear congruential method; tests for randomness; generation of random variate
Mathematical and statistical models:
—Stochastic processes; estimation of mean, variance and correlation; confidence interval; standard error, and hypothesis testing
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
Analysis of simulation data:
—Input modeling; data collection; sample mean; sample variance
—Parameter estimation, sensitivity analysis
Monte Carlo simulation and applications
Markov Chain and applications
Calibration, Verification and Validation of Simulation Models:
—Validation of model assumptions; validation of input-output transformation, calibration of the model
For more info and registration, please contact Dr. Vakilzadian (hvakilzadian@unl.edu).