ISE789 Syllabus

ISE 789: Syllabus

Instructor

Professor S.-C. Fang (fang@ncsu.edu)

  • Office
    450 | 111 Lampe Drive
    919.515.2192
  • Office Hours
    Tu, Th 2:30 – 3:30 PM (or by appointment)

Teaching Assistant

Ye Tian

Prerequisites

OR501 Introduction to Operations Research or equivalent

Course Objectives

This course is to introduce the basic concepts of soft computing methodology and to provide hand-on experience for problem solving. We shall spend about 4 weeks on fuzzy sets and systems, 3 weeks on neural networks, 5 weeks on genetic algorithms, simulated annealing, tabu search and other methods.

Course Contents

  1. Fuzzy Sets and Systems
  2. Neural Networks
  3. Genetic Algorithms
  4. Simulated Annealing
  5. Tabu Search
  6. Other Methods

Grades

  • Homeworks – 20%
  • Projects – 30%
  • Midterm – 20%
  • Final – 30%

Evaluation Standard

  • A – 88 and above
  • B – 75 to 87
  • C – 60 to 74
  • Fail – under 60

Homework

  • About 7 assignments.
  • Solution sets will be posted by TA.
  • Rule 1: No late homework without TA’s approval
  • Rule 2: Convince TA for any grade changes.

Exams

  • Rule 3: No make-up exam without my pre-approval or “emergency notes” from hospital.
  • Midterm: in-class with one 4×6 index card.
  • Final: Comprehensive take home.

Textbooks

H.-J. Zimmermann, “Fuzzy Set Theory and Its Applications” (QA248.Z55 1996), Kluwer Academic Publishers, 1996, 3rd Edition.

Murray Smith, “Neural Networks for Statistical Modeling” (QA76.87.S62 1993), International Thomson Computer Press, 1993.

J.-S.R. Jang, C.-T. Sun and E. Mizutani: “Neuro-Fuzzy and Soft Computing” (QA76.9.S63J36 1997), Prentice Hall, 1997.

Mitsuo Gen and Runwei Cheng, “Genetic Algorithms & Engineering Optimization” (T56.24.G46 2000), Wiley-Interscience, 2000.