Decision Support Systems Using Intelligent Paradigms
  Decision-making is a process of choosing among alternative courses of action
for solving complicated problems where multi-criteria objectives are involved.
The past few years have witnessed a growing recognition of Soft Computing (SC)
technologies that underlie the conception, design and utilization of
intelligent systems. In this paper, we present different SC paradigms involving
an artificial neural network trained using the scaled conjugate gradient
algorithm, two different fuzzy inference methods optimised using neural network
learning/evolutionary algorithms and regression trees for developing
intelligent decision support systems. We demonstrate the efficiency of the
different algorithms by developing a decision support system for a Tactical Air
Combat Environment (TACE). Some empirical comparisons between the different
algorithms are also provided.
