An Outline of Our Research

During the last two decades, the literature is flooded with the studies applying computational intelligence (i.e. numerical methods for implementing an intelligent behavior) to understand the complex and uncertain behavior of real-world processes. Despite an increased research interest in neuro/fuzzy modeling techniques, the field lacks a mathematical framework for the design and analysis of intelligent systems taking into account the underlying uncertainties in a sensible way to deal with the realworld problems. We propose, with applications to environmental science, medicine, chemistry and drug design, a modeling framework offering the possibilities of

# the design of a fuzzy filter, based on different mathematical criteria, for filtering out the uncertainties from the modeling problem;

# a mathematical analysis of the filltering methods with emphasis on stability, robustness, and steady-state error issues;

# utilizing the data about uncertainties provided by the fuzzy filter for an understanding of the process behavior.

Our investigations have shown that the standard neuro/fuzzy modeling techniques fail in many modeling problems to describe the process behavior. And the proposed modeling approach could potentially solve these complex and uncertain problems. The studies on several application examples related to the life science have verified the feasibility of our approach.

Our work provides a mathematical theory to contribute in the development of research area "Intelligent Fuzzy Computing" with applications to modeling problems related to the life science. The presented theory can be used to develop a fuzzy filtering based system for data modeling in presence of uncertainties.

Research Applications in Life Science