Course
Pro-forma
Bachelor of Engineering (Computer Aided Design & Manufacturing)
Code
KCEP4310
Title
Computational Intelligence for Engineering and Manufacture
Pre-requisite
None
Student Learning Time (SLT)
120 hours
Credit
3
Learning Outcomes
1.
Describe the Principals of Computational Intelligence methods
such as neural networks and population based metaheuristic
algorithms
2.
Apply the concepts of computational intelligence methods such
as neural networks and genetic algorithms to engineering
problems.
3.
Differentiate different neural networks models using diagram
and illustrations.
4.
Categorize neural networks models for learning and
metaheuristic algorithms used for optimization.
5.
Evaluate the performance of multi-layered neural networks and
genetic algorithm optimization procedure.
Synopsis
The course starts with an introduction on computational intelligence
and optimization. The first part of the course concentrates on
artificial neural networks as computational tools for classification,
prediction and approximation. The neural networks concepts
(architecture, learning methods, and approaches) will be presented.
The Backpropagation learning algorithm for neural network training
will be explained in detail in addition to appropriate training
procedures. Other neural networks models such as Hopfield and
Kohonen networks will be given brief introductions.
The applications of neural networks to engineering design and
analysis will be presented through a number of case studies.
The second part of the course concentrates on metaheuristic
algorithms for solving optimization problems. The course
concentrates on population-based metaheuristic algorithms in
particular genetic algorithms. The genetic algorithm concepts
(reproduction, cross-over and mutation) in addition t
Assessment
40 % Continuous Assessments
60 % Final Examination
References
1.
Anupam Shukla, Ritu Tiwari, Rahul Kala. Real Life Applications of
Soft Computing. CRC Press, ISBN: 1439822875 (2010)
2.
D.E. Goldberg. Genetic Algorithms In Search, Optimization &
Machine Learning. Pearson Education (singapore) Pte. Ltd. ISBN:
817758829X (2008)
3.
Mitsuo Gen, Runwei Cheng. Genetic Algorithms and Engineering
Design. John Wiley & Sons, Inc. ISBN: 9780471127413. (1997)
4.
Jun Wang, Qing Dao and Andrew Kusiak (Editors).
Computational Intelligence in Manufacturing Handbook. CRC
Press. ISBN: 9780849305924 (2000)
Soft Skills
Critical Thinking and Problem Solving Skills (CT1, CT2, CT3)