|This course introduces soft computing methods which, unlike hard computing, are tolerant of imprecision, uncertainty and partial truth. The principal constituents of soft computing are fuzzy logic, neural network theory, support vector machines and genetic algorithms.
|1. Understand the difference between hard and soft computing methods.
2. Be able to apply several soft computing techniques for learning from experimental data.
3. Understand what is fuzzy logic and apply it in various applications
4. Apply neural networks to solve soft computing problems
5. Model problems using support vector machine
6. Use genetic algorithms to solve soft computing problems
|Kecman, V., Learning and Soft Computing, The MIT Press, Cambridge, MA.
|arabic ref. books
|karray F. and Silva C., Soft Computing and Intelligent Systems Design - Theory, Tools and Applications, Addison Wesley.