Export Behaviour Modeling Using EvoNF Approach

TL;DR

This paper introduces an EvoNF approach combining fuzzy inference, neural networks, and evolutionary algorithms to model and predict export behaviors of multinational subsidiaries more accurately.

Contribution

It presents a novel EvoNF method that optimizes fuzzy inference systems with neural and evolutionary techniques for export behavior modeling.

Findings

EvoNF outperforms direct neural network models in accuracy.

The approach effectively captures complex export patterns.

Empirical results validate the model's predictive capability.

Abstract

The academic literature suggests that the extent of exporting by multinational corporation subsidiaries (MCS) depends on their product manufactured, resources, tax protection, customers and markets, involvement strategy, financial independence and suppliers' relationship with a multinational corporation (MNC). The aim of this paper is to model the complex export pattern behaviour using a Takagi-Sugeno fuzzy inference system in order to determine the actual volume of MCS export output (sales exported). The proposed fuzzy inference system is optimised by using neural network learning and evolutionary computation. Empirical results clearly show that the proposed approach could model the export behaviour reasonable well compared to a direct neural network approach.