A Concurrent Fuzzy-Neural Network Approach for Decision Support Systems

TL;DR

This paper introduces a concurrent fuzzy-neural network method combining unsupervised and supervised learning to enhance decision support systems, specifically demonstrated on a tactical air combat decision system with promising results.

Contribution

It presents a novel concurrent fuzzy-neural network approach integrating multiple learning techniques for improved decision support systems.

Findings

Demonstrated efficiency of the proposed method.

Applied approach to Tactical Air Combat Decision Support System.

Results show improved decision-making performance.

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 technologies that underlie the conception, design and utilization of intelligent systems. Several works have been done where engineers and scientists have applied intelligent techniques and heuristics to obtain optimal decisions from imprecise information. In this paper, we present a concurrent fuzzy-neural network approach combining unsupervised and supervised learning techniques to develop the Tactical Air Combat Decision Support System (TACDSS). Experiment results clearly demonstrate the efficiency of the proposed technique.