EGR 301, Fall 2004
Modeling and Simulation of Natural and Engineered Systems

Professors email Office Office Hours
Judith Cardell EGR 105b Monday: 1:00 - 2:30
Wednesday: 11:00 - noon
Glenn Ellis EGR 105a Monday: 9:00 - 10:00am
Tuesday: 2:30 - 3:30

Class Time: TR 9:00 - 10:20, EGR 201

Prerequisites: Physics 210, corequisites Math 204, EGR 320

Text: Introduction to Dynamic Systems: Theory, Models & Applications, by David G. Luenberger; Wiley, 1979, ISBN 0-471-02594-1

Course Overview:
The objective of this course is to introduce students to the fundamental theory, mathematics and modeling tools necessary to analyze and simulate natural and engineered systems. The course includes three broad areas of modeling and analysis: that of stationary processes, linear dynamic systems and neural networks.

Topics include modeling time series with ARIMA models, applications of artificial neural networks, building state space models for dynamic systems, and performing sensitivity and stability analyses. Students will have the opportunity to apply these tools to model systems in many areas of engineering, including: mechanical, civil, environmental, computer, chemical and electrical engineering.

Simple examples of this type of system include systems with masses and springs (mechanical), systems with resistors and energy storage devices (electrical circuits with capacitance and inductance), heat transfer (thermal), capacitors and resistors (fluid systems), energy converting and signal converting devices (mixed systems). More advanced examples of systems that could be analyzed include earthquake ground motion, water and wastewater treatment, financial markets, pendulums, robotic arms, spacecraft, electric power systems, the sustainable use of natural resources, the human body and natural waterways, to mention only a few.

Through the material presented in this course, students will be able to

  1. Recognize engineering applications for ARIMA models, input-output and state-space models for linear dynamic systems, and artificial neural networks.
  2. Identify time series data in the world around them; describe the properties of the time series; fit an ARIMA model to the time series; and apply the model for forecasting and simulation.
  3. Identify and define linear dynamic systems in the world around them. Model and predict the behavior of these systems with respect to the system inputs, system evolution and the system outputs.
  4. Develop a basic understanding of the concepts of stability and feedback control for linear dynamic systems.
  5. Construct, train, and test feed-forward, back-propagation artificial neural networks to learn relationships among variables.
  6. Identify both the appropriate applications and the limitations of each model developed.
  7. Learn and use modeling tools, including Matlab and associated toolboxes.
  8. Present project results, including the problem formulation and analysis method selected, through oral and written formats.

Course Documents

EGR 301 Schedule - Weekly Summary, Spring 2004

Week Topic Reading Assignments
Jan 25 Introduction to course topics;
Matlab review
Dynamic Systems Slides
Matlab Review Slides
See Matlab links above, as needed (HW 1 out)
Jan 27 Artificial Neural Networks Basics
ANN Slides
Introduction to Neural Networks handout Project 1
Feb 1 Artificial Neural Networks Basics   HW 1 due
HW1.m Soln
HW1results.txt Soln
Feb 3 Training ANNs Backpropogation handout bodyfat.mat
Feb 8 Training ANNs    
Feb 10 ANN Applications   HW 2 due
HW 2 data set
Feb 15 AI and philosophy of the mind Handouts  
Feb 17 AI and philosophy of the mind   HW 3 due
Feb 22 Introduction to linear dynamic systems Chapter 1 of Luenberger EXAM 1 on ANN,
in Bass library
Due 4pm Feb 25
Feb 24 Difference equations Chapter 2: 2.1 - 2.7 Project 2 out
Mar 1 Differential equations and applications;
Student system examples (from HW4)
Chapter 2: 2.8 - 2.10 HW 4 due
Mar 3 Linear Algebra; Matrix transformations Chapter 3: 3.1 - 3.5  
Mar 8 Matlab transformation examples Chapter 3: 3.1 - 3.5 HW 5 due
Mar 10 ANN Student Presentations   Project 1 due
Mar 15 Spring Break Yeah!
Mar 17 Spring Break Yeah!
Mar 22 Eigenvectors and eigenvalues;
Matlab transformation examples
Chapter 3: 3.6 - 3.9  
Mar 24 State space and state equations;
Block diagrams
Chapter 4: 4.1 - 4.3 HW 5 part 2 due
Mar 29 General solutions to discrete and continuous time systems Chapter 4: 4.4 - 4.7  
Mar 31 Linear systems: Diagonalization, equilibrium Chapter 5: 5.1 - 5.6 HW 6 due
HW 6 Matlab Tip
Apr 5 Linear systems: Stability, oscillation and dominant modes Chapter 5: 5.7 - 5.11  
Apr 7 Linear Systems Applications;
Class examples of System Dynamics
Chapter 5: 5-13 HW 7 due
Romeo & Juliet examples
Apr 12 Sensitivity analysis Handout EXAM 2: Dynamic Systems
(in Bass library)
Wed 4/13 - Sat 4/16
Apr 14 Correlation Handouts  
Apr 19 ARIMA models    
Apr 21 Fitting ARIMA models ARIMA Examples  
Apr 26 Project 2 Presentations: Dynamic Systems   HW 8 due
Apr 28 Fitting ARIMA models ARIMApractice.MPJ Project 2 reports due, 4pm to EGR 105B
Final Exam During Finals Week Pickup: Starting at 9am Friday, April 29 from Bass Library Hand in:
4:00pm Thursday,
May 5 to Michele Schaft in Little House

Exams handed in between
4:00 and 4:30 will lose 1
point per minute

Exams handed in
after 4:30 will not be accepted