CE 5250 Special Topics: System Identification 

Professor: Dr. Andrew Swartz, Ph.D. 201D Dillman Hall (906) 4872439; raswartz@mtu.edu Office Hours: Monday 11AMnoon, Wednesday and Thursday 910 AM, or by arrangement.


[ Meeting Times  Text and Syllabus  Description  Prerequisites  Grading  Policies  Homework  Notes] 

Lecture Hours: MWF 2:05  2:55 PM; Dillman 214 Textbook: Subspace Methods for System Identification: Katayama (2010) Course Syllabus (pdf)


Course Description:
This course it intended to introduce to the student the basic tools required for the process of system identification. The system identification process allows engineers to develop datadriven models mapping system inputs to measured outputs for systems that may be too complicated to model mechanistically or those with mechanistic models that require validation or refinement.


Prerequisites:
Graduate standing. It is expected that all students
understand (and are able to apply) general concepts from integral and
differential calculus, linear algebra, statistics and probability, as well as
partial differential equations as required during the course of an ABET
accredited undergraduate degree in engineering.


Homework Assignments (about 8) ........................................................... Exam (Midterm  Take home) ................................................................... Final Project ................................................................................................ Total ...........................................................................................................

40% 25% 35% 100% 
Attendance: Attendance is required. Exams: There will be one exam midway through the semester. The exam will be a take home exam with the duration and timing to be determined using input from the class. Computing Policy: It is expected that students will have access to a computer running Matlab for homework assignments, the exam, as well as the final project. Final Project: Students will complete a final project applying the system identification techniques learned in class to a topic selected by the student. In the course of the project, students will form models from data make observations about the models that they have made. Students will present their findings in a report. Topics may be drawn from existing research projects or other areas of interest to the student. Homework: All homework will be collected on the date due. Homework may be turned in inclass or overnight under my office door (201D Dillman Hall). Assignments should be written neatly and legibly. Begin each new problem on a new page. Supporting work and Matlab code is necessary to receive credit. Detailed assignment guidelines specific to each homework set will be distributed via the course website. Scheduling of the Final Exam: There is no final exam for this course. Collaboration Policy: Collaboration on homework sets is encouraged. Multistudent homework collaborations (i.e., a single solution set submitted for multiple students) will be accepted so long as the students represented agree to share the same grade for the assignment. Exams are to be strictly noncollaborative. Course Email List: Dr. Swartz maintains an email list for CE5250 to share information he thinks may benefit the class on an adhoc basis. Dr. Swartz considers this information to be a supplement to the classroom experience, not a replacement for lecture attendance. Final Grade Basis: A 93 – 100 AB 87 – 92.9 B 83 – 86.9 BC 77 – 82.9 C 73 – 76.9 CD 67 – 72.9 D 63 – 66.9 F Below 63 ADA Statement: “MTU complies with all federal and state laws and regulations regarding discrimination, including the Americans with Disabilities Act of 1990.” (ADA) If you have a disability and need a reasonable accommodation for equal access to education or services at MTU, please call the Dean of Students at 4872212. For other concerns about discrimination, you may contact your advisor, department head or the Affirmative Action Office at 4873310. 

Homework
Assignments:


Course Notes:
Lesson 0  Introduction Lesson 1  Signals and Systems Lesson 2  Fourier Analysis Lesson 3  Sampling and Discrete Fourier Transform Lesson 4  Aliasing and Windowing Lesson 5  Frequency Domain Representation of LTI Systems and Convolution Lesson 6  Behavior of LTI Systems Lesson 7  MDOF LTI Systems Lesson 8  The ZTransform Lecture m.files: 1DOF System; 2DOF System Lesson 9  DT Transfer Function Models Lesson 10  ARX Models and LeastSquares Projection Lesson 11  Recursive LeastSquares for ARX Lesson 12  Model Size Evaluation Lesson 13  Feedback Systems Lesson 14  MIMO v. SISO Lesson 15  Moving Average Models Lesson 16  Introduction to StateSpace Models Lesson 17  DT StateSpace Models Lesson 18  Features of StateSpace Models Lesson 19  StateSpace Realization Preliminaries Lesson 20  ERA Lesson 21  Advanced System ID Topics Lesson 22  Introduction to the Kalman Filter

