Soft Computing Homework
ISE789 Soft Computing
Syllabus
References
Lecture Notes
Soft Computing
NN Chapter 1 | NN Chapter 2 | GA Chapter 1 | GA Chapter 2
Simulated Annealing | Tabu Search | Tropical Cyclone-based Method | Electromagnetic Method
Fuzzy Introduction | Fuzzy Short Course | Building Fuzzy Expert Systems
Fuzzy Regression | Fuzzy Relation Equation | Fuzzy Clustering
Supplemental Material
- Approximation by Superpositions of a Sigmoidal Function
- Multilayer Feedforward Networks With A Non-polynomial Activation Function Can Approximate Any Function
- Relaxed conditions for radial-basis function networks to be universal approximators
- Universal Approximation Using Radial-Basis-Function Networks
- An Introduction To Computing With Neural Nets
- A neural network model with bounded-weights for pattern classification
- Solving Convex Programming Problems with Equality Constraints by Neural Networks
- Neurocomputing with Time Delay Analysis for Solving Convex Quadratic Programming Problems
- Efficient Neural Network Learning using Second Order Information with Fuzzy Control
- Sequencing parallel machining operations by genetic algorithms
- A genetic-based framework for solving (multi-criteria) weighted matching problems
- A Genetic Algorithm Approach to Solving DNA Fragment Assembly Problem
- A tropical cyclone-based method for global optimization
- An electromagnetism-like mechanism for global optimization
- On the convergence of a population-based global optimization algorithm
- Ant colony optimization theory: A survey| Ant colony optimization
- Particle swarm optimization
- Induction of fuzzy rules and membership functions from training examples
- Expert System | Induction of fuzzy rules and membership functions
- Fuzzy Regression | Fuzzy Clustering | Fuzzy Relational Equation
Homework
Assignment #1 | Assignment #2 | Assignment #3 | Assignment #4
Assignment #5 | Assignment #6 | Assignment #7 | Assignment #8
Project
Project requirements
Optional Project
Exam (Last week of March)
Course Grade
Cleveland State University
Department of Electrical and Computer Engineering
EEC 645/745, ESC 794
Intelligent Control Systems
Syllabus, Fall 2010
Instructor:
Dan Simon
Telephone: 216-687-2589
E-mail: d.j.simon@csuohio.edu
Web: http://academic.csuohio.edu/simond/courses/eec645
Prerequisites:
EEC 440 (Control Systems) and EEC 510 (Linear Systems), or permission of instructor
Catalog:
Prerequisite: EEC 510. Artificial intelligence techinques applied to control system design. Topics include fuzzy sets, artificial neural networks, methods for designing fuzzy-logic controllers and neural network controllers; application of computer-aided design techniques for designing fuzzy-logic and neural-network controllers.
Textbook:
J-S. R. Jang, C-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice Hall, 1997, http://mirlab.org/jang/book/
References:
R. A. Aliev and R. R. Aliev, Soft Computing & Its Applications, World Scientific Publishing Company, 2001
Clive L. Dym and Raymond E. Levitt, Knowledge-Based Systems in Engineering, McGraw-Hill, 1991
Adrian A. Hopgood, Knowledge-Based Systems for Engineersand Scientists, CRC Press, 1993
Stamatios V. Kartalopoulos, Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications, Wiley-IEEE Press, 1995
Vojislav Kecman, Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models, The MIT Press, 2001
Amit Konar, Computational Intelligence: Principles, Techniques and Applications, Springer, 2005
T. Nanayakkara, F. Sahin, and M. Jamshidi, Intelligent Control Systems with an Introduction to Systems of Systems, CRC Press, 2008
Sankar K. Pal and Sushmita Mitra, Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing, John Wiley & Sons, 1999
Antonio Ruano, Intelligent Control Systems Using Computational Intelligence Techniques, Institution of Engineering and Technology, 2005
Y. Sin and C. Xu, Intelligent Systems: Modeling, Optimization, and Control, CRC Press, 2008
Lefteri H. Tsoukalas and Robert E. Uhrig, Fuzzy and Neural Approaches in Engineering, Wiley-Interscience, 1997
Objectives: Students completing this course will obtain a basic understanding of fuzzy logic systems and artificial neural networks, and will know how these techniques are applied to engineering problems, including control systems. Students will understand the advantages and disadvantages of these methods relative to other control methods. Students will be aware of current research trends and issues. Students will be able to design control systems using fuzzy logic and artificial neural networks.
Grading | Masters | Doctoral |
Homework | 25% | 20% |
Midterm | 25% | 20% |
Term Project | 25% | 20% |
Final Exam | 25% | 20% |
Technical Paper | -- | 20% |
Homework: Homework assignments will be posted at http://academic.csuohio.edu/simond/courses/eec645/homework.html. It each student’s responsibility to keep track of the homework assignments and due dates.
Doctoral Students: Doctoral students are required to write a technical paper appropriate for journal submission.
Paper Submission: Students should submit their term project and technical paper at www.turnitin.com. This web site will help us make sure that the assignments do not contain any plagiarism. The class id is 3421538 and the password is neurofuzzy.
Schedule:
Professor Simon’s Home Page
Department of Electrical and Computer Engineering
Cleveland State University
Last Revised: November 18, 2010
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