New Research Perspectives in the Emerging Field of Computational Intelligence to Economic Modeling

Computational Intelligence (CI) is a new development paradigm of intelligent systems which has resulted from a synergy between fuzzy sets, artificial neural networks, evolutionary computation, machine learning, etc., broadening computer science, physics, economics, engineering, mathematics, statistics. It is imperative to know why these tools can be potentially relevant and effective to economic and financial modeling. This paper presents, after a synergic new paradigm of intelligent systems, as a practical case study the fuzzy and temporal properties of knowledge formalism embedded in an Intelligent Control System (ICS), based on FT-algorithm. We are not dealing high with level reasoning methods, because we think that real-time problems can only be solved by rather low-level reasoning. Most of the overall run-time of fuzzy expert systems is used in the match phase. To achieve a fast reasoning the number of fuzzy set operations must be reduced. For this, we use a fuzzy compiled structure of knowledge, like Rete, because it is required for real-time responses. Solving the match-time predictability problem would allow us to built much more powerful reasoning techniques.