Course Outline System Simulation cs-454 Spring 2016 Introduction
Download 14.9 Kb.
|
- Navigate this page:
- Course objectives
- Instructor
- Special notice
- Special Note for Students with Disability
- Acknowledgments
|
Course Outline System Simulation CS-454 Spring 2016 Introduction: Modeling is the fundamental process to understand, design and communicate about systems and its related components. Every object of interest in computer technology is a product of human thought, and is, therefore, amenable to analysis and design as a system; modeling is the framework in which this activity is formalized with a very specific goal in mind. A model is best studied through its simulation in which all its possible nuances could be explored via a WHAT-IF type set of approaches allowing us to smooth it down to work in a practical setting. Simulation is also a way to check the stated performance of a system without actually building it; and therefore, it does provide a rational basis for system design architecture in a substantial way. In this course, we indicate usage of simulation techniques in both discrete and continuous systems, respectively. Course objectives:
Instructor: Sam Sengupta, C129, Kunsela, phone 792-7353 (office), 735-0874 (residence). email: sengupta@sunyit.edu Lecture schedule: Mondays and Wednesdays @ C006 (Kunsela) 2:00PM-3:50PM Office hours: Mondays and Thursdays: 11:00 PM – 1:50 PM Textbook: No textbook is planned. Lecture Notes would be available on the class webpage at http://sunyit.edu/~sengupta/class_notes/CS454 Special notice: Special notices regarding the class would be announced on our class webpage. Course outline: 1. Introduction to Simulation 2. Some basic models: a. Cash-flow models b. Inventory control models c. Differential models d. Stability models e. Neural models (if time permits) 3. Random number generators a. Uniformly distributed random number b. Testing of uniformly distributed variables c. Generation of non-uniform variables 4. Time-series analysis a. Autoregressive models for stationary processes b. Time series models for seasonal data c. Moving average models 5. Markov Chains (if time permits) a. Chapman-Kolmogorov equations b. Regular Markov chains c. Applications 6. Basic queuing systems (if time permits) a. Poisson process b. M/M/1 c. M/M/1/k d. M/M/c e. M/G/1 f. Johnson Queues 7. Discrete systems using GPSS 8. Continuous Systems dynamics 8. Continuous systems using Analog systems 9. Conceptual systems 9. Analysis of simulation outputs Evaluation: There will be one midterm and one final exam. In addition to these, each student would be assigned four assignments that he/she would research on and then present this to the class on a seminar/workshop/tutorial mode in order for the class to learn from it. The exams would be each out of 100, and the assignments would each be out of 50. The final performance grade would be computed by dividing the total marks earned by 4. Standard letter grade mode would apply on the final reported grade. Special Note for Students with Disability: In compliance with the Americans with Disabilities Act of 1990 and with Section 504 of the Rehabilitation Act, SUNY Polytechnic Institute is committed to ensuring educational access and accommodations for all its registered students seeking access to meet course requirements and fully participate in programs or activities. SUNY Poly students with documented disabilities and medical conditions are encouraged to request these services by registering with the Disability Services Office and discussing your need for accommodations. For information or an appointment contact Suzanne Sprague at the Disability Services Office, located in room B101, Kunsela Hall or by phone (315)792-7170, or by email suzanne.sprague@sunyit.edu. Acknowledgments: Lecture materials and video links suggested for use in this course are obtained from various sources as I saw fit. Wherever possible, I’ve tried to acknowledge them in my class website. Students Learning Outcome: ■ A student should show proficiency in simulating discrete event models ■ A student should demonstrate proficiency in simulating and analyzing continuous systems models ■ A student should show capacity to formulate analytical systems models using differential equations. Download 14.9 Kb. Share with your friends:
|