Life science applications of computational intelligence for modelling

Can computers be intelligent? If yes! Then how to represent intelligence? The
development of digital computers made possible the invention of human engineered
systems that show intelligent behaviour. Now a days, the researchers are active with the
studies applying computational intelligence (i.e. numerical methods for implementing an
intelligent behaviour) to understand the complex and uncertain behaviour of real-world
processes. Despite advancement in neuro/fuzzy modeling techniques, the field still lacks
a mathematical framework for the design and analysis of intelligent systems to deal with
the real-world problems considering the underlying uncertainties in a sensible way. This
thesis presents a fuzzy rules based system for modeling the relationships between inputs
and output data in the presence of uncertainties. The fuzzy system is designed by
separating the uncertainties from the data using fuzzy filtering algorithms. A stochastic
modeling of the uncertainties helps in designing the fuzzy system to approximate the
uncertain relationships. The proposed fuzzy model offers the followings.