Outline of a Course on Computational Intelligence Claudio Moraga University of Dortmund Germany JEP Bitola Workshop December 2003

The world of Computational Intelligence Fuzzy Logic Neural networks Evol. Algor. Probabilities and statistics © cm

Artificial Intelligence v/s Computational Intelligence Artificial Intelligence Computational Intelligence Artificial Intelligence Computational Intelligence © cm

Neural Networks 1.Basics: Definitions. The point of view of neurophysiology; the point of view of Approximation Theory. Historical development. Types of neural networks from the point of view of structure, information flow, transfer function of elementary units. Feed-forward neural networks. Training algorithms. Learning and generalizing. 2.Classification of patterns and approximation of functions. Design of experiments. Statistical considerations for the evaluation of results Discussion of examples 3. New applications (e.g. Blind Source Separation) © cm

An environment to learn and design Neural networks SNNS: Stuttgart neural networks simulator Comprises: Different types of neural networks Different training algorithms Several representations of weights and outputs Demonstration examples Well documented handbook (in English) SNNS is public domain © cm

Fuzzy Logic 1.Basics Historical development; classic sets and fuzzy sets 2.Operations with fuzzy sets Basic fuzzy connectives. The concept of t-norms and t-conorms Generalized Modus Ponens; fuzzy inference Compositional Rule of Inference 3.Applications Work with Linguistic Variables; modeling with fuzzy if-then rules Fuzzy decision making Fuzzy control © cm

An environment to design fuzzy logic systems Xfuzzy 3.0 “This new version of Xfuzzy is based on a new specification language (XFL3) which extends the advantages of its predecessor, allowing the use of linguistic hedges as well as new fuzzy operators defined freely by the user. New CAD tools have been included to ease the edition of operator sets and hierarchical systems, to generate 2- and 3-dimensional graphic outputs, and to monitor the inference process. The tool that applies supervised learning has been quitely renewed so as to include new algorithms as well as pre- and post-processing techniques to simplify the obtained rule bases. Xfuzzy 3.0 has been enterely programmed in Java. Hence, it can be executed on any platform with JRE (Java Runtime Environment) installed.” Xfuzzy is public domain © cm

Evolutionary Algorithms This area comprises: Evolutionary strategies (Rechenberg, Schwefel) Genetic Algorithms (Holland) Evolutionary computing (Fogel) Simulated Annealing Ant colonies, swarms Basics, structure of the algorithms, main variations Comparative analysis of test examples © cm

An environment to work with Genetic Algorithms GALIB GAlib contains a set of C++ genetic algorithm objects. The library includes tools for using genetic algorithms to do optimization in any C++ program using any representation and genetic operators. The documentation includes an extensive overview of how to implement a genetic algorithm as well as examples illustrating customizations to the GAlib classes. GAlib has been built on various UNIX platforms (SGI, Sun, HP, DEC, IBM) as well as MacOS and DOS/Windows systems. GAlib includes examples that use PVM for distributed, parallel implementations. GAlib includes graphic examples that use the Athena or Motif widget sets, or MFC/VC++. or ftp://lancet.mit.edu/pub/ga/ GALIB is public domain © cm

Hybrid Systems Hybrid Systems combine different approaches in a search for synergy Neuro-fuzzy Systems Neural networks with fuzzy data Genetic Algorithms with fuzzy fitness Evolutionary optimization of fuzzy systems Evolutionary design of neural networks Robust neural networks Working with fuzzy probabilities © cm

Closing Remarks The former proposal is an updated version of an elective Course offered by the author at the Department of Computer Science of the University of Dortmund. Schedule: 1 semester at 2 hours per week (lecture) plus 2 hours per week (exercises, mainly with the design environments). Pre-requirements: Students should have programming experience Students should have passed all basic Mathematics Courses (mainly Differential Calculus, Linear Algebra, and Probabilities and Statistics) © cm

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