Decision Support Systems Using Intelligent Paradigms

TL;DR

This paper explores various soft computing paradigms, including neural networks, fuzzy inference, and regression trees, to develop intelligent decision support systems, demonstrating their efficiency through a Tactical Air Combat Environment case study.

Contribution

It introduces and compares multiple soft computing techniques for decision support systems, highlighting their practical application and effectiveness in a complex military scenario.

Findings

Neural networks trained with scaled conjugate gradient are effective.

Fuzzy inference methods optimized with neural networks perform well.

Regression trees provide a viable alternative for decision support.

Abstract

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.