Behavior Modeling of Coded Networks in Dynamic Networks
Farzad Amirjavid, Abdenour Bouzouane, Bruno Bouchard · 2012
Predicting, considering and designing all of the possible states of real world problems that are justified in artificial intelligence domain is relatively difficult or rather impossible for the experts. One reason is that the real world problems are highly complicated and they depend on a lot of variables. Furthermore, they do not practice their behavior as similar as their past comportments and it is rare that we find a linear behavior from such mentioned problems. We propose to apply data-driven data mining approaches to learn non-linear systems' behaviors (rather than expert's knowledge driven ones), so we could define delicate fuzzy states to indicate the system behavior. Intelligence may be a reason of non-linearly behavior of real world problems. The more intelligence is with a system, more variables are included in its behavior and a more non-linear behavior is expected. Observing a lot of variables and features concerning to the behavior of intelligent systems, would lead to a relatively great data warehouse. Analyzing this data we would model the behavior of non-linear systems and finally we would propose an optimization approach for non-linear systems to achieve their goals. As a case-study, a network coding problem is explained to illustrate well the proposed approach.