• 089217 - SOFT COMPUTING

Goals

Soft Computing includes technologies (Fuzzy Systems, Neural Networks, Stochastic Algorithms and models) to model complex systems and offers powerful modeling tools for engineers and in general people needing to model phenomena. Among the application areas, we mention: data analysis, automatic control, modeling of artificial and natural phenomena, modeling of behaviors (e.g., of users and devices), decision support. The course will introduce rigorously the fundamentals of the different modeling approaches, will put in evidence the application possibilities, by comparing different models, examples and application cases, will introduce design techniques for systems based on these technologies. No specific background is required.

Contents

What is Soft Computing: fuzzy systems, neural networks, stochastic algorithms and models - Fuzzy models: fuzzy sets, fuzzy logic, fuzzy rules, motivations for fuzzy modeling -Neural networks: basics, supervised and unsuprvised learning, main models, selection and evaluation. Stochastic models: basics, optimization of models, fitness function, model definition, genetic algorithms, reinforcement learning, bayesian networks.  Applications: motivations, choices, models, case studies.

The evaluation consists in a written exam where both theoreticalcompetence  and design skills will be tested.