Consider taking ELEC 498/898 Computational Modeling and Simulation I (Discrete Systems) in the spring semester. It will be taught MF 8:15-9:30 a.m. The call # is ELEC 498 (7055); ELEC 898 (7056).
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 its results using MathLab. Modeling technciques, varification and validation in the context of a design, will be also covered.
Course Outline
Intro to discrete-event simulation
Areas of application, systems environment, components of a system
Model of a system, types of model
Concepts in discrete-event simulation
Event scheduling and time advance scheduling
Basic list processing operations
Review of basic probability and statistics concepts
PMF, CDF, central limit theorem, laws of large numbers
Expectation, variance, probability distribution functions and their properties
Random number generation
Linear congruential method, tests for randomness
Random variable generation (inverse transform, exponential distribution)
Mathematical and statistical models
Simulation output data and stochastic processes
Estimation of mean, variances, and correlation
Confidence interval and hypothesis testing
Useful statistical models
Queuing models
Characteristics of queuing systems; queuing notation; single and multiple server queues; steady state behavior of infinite and finite population models; networks of queues
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 its results using MathLab. Modeling technciques, varification and validation in the context of a design, will be also covered.
Prereq: Prerequisite or in parallel ELEC 305 or equivalent course in probability and statistics, a course in high level programming language
Analysis of simulation data
Input modeling; data collection; sample mean; sample variance
Verification and Validation of Simulation Models
Validation of model assumptions
Validation of input-output transformation
Monte Carlo Simulation and its application
For more info and registration, please contact Dr. Vakilzadian (hvakilzadian@unl.edu)