Research Article Open Access

A NOVEL APPROACH BASED ON GENETIC FUZZY CLUSTERING AND ADAPTIVE NEURAL NETWORKS FOR SALES FORECASTING

Attariuas Hichama1, Bouhorma Mohameda1 and El Fallahi Abdellahb1
  • 1 Abdelmalek Essaadi University, Morocco

Abstract

This article proposes a new hybrid sales forecasting system based on genetic fuzzy clustering and Back-Propagation (BP) Neural Networks with adaptive learning rate (GFCBPN).The proposed architecture consists of three stages: (1) utilizing Winter’s Exponential Smoothing method and Fuzzy C-Means clustering, all normalized data records will be categorized into k clusters; (2) using an adapted Genetic Fuzzy System (MCGFS), the fuzzy rules of membership levels to each cluster will be extracted; (3) each cluster will be fed into parallel BP networks with a learning rate adapted as the level of cluster membership of training data records. Compared to previous researches which use Hard clustering, this research uses the fuzzy clustering which capable to increase the number of elements of each cluster and consequently improve the accuracy of the proposed forecasting system. Printed Circuit Board (PCB) will be utilized as a case study to evaluate the precision of our proposed system. Experimental results show that the proposed model outperforms the previous and traditional approaches. Therefore, it is a very promising method for financial forecasting.

Journal of Computer Science
Volume 9 No. 8, 2013, 949-966

DOI: https://doi.org/10.3844/jcssp.2013.949.966

Submitted On: 24 December 2012 Published On: 2 July 2013

How to Cite: Hichama, A., Mohameda, B. & Abdellahb, E. F. (2013). A NOVEL APPROACH BASED ON GENETIC FUZZY CLUSTERING AND ADAPTIVE NEURAL NETWORKS FOR SALES FORECASTING. Journal of Computer Science, 9(8), 949-966. https://doi.org/10.3844/jcssp.2013.949.966

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Keywords

  • Sales Forecasting
  • Fuzzy Clustering
  • Genetic Fuzzy System
  • Printed Circuit Boards
  • Back Propagation Network
  • Hybrid Intelligence Approach