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Fuzzy Logic and Back-Propagation Neural Networks for Optimal Performance

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  • Abbas Al-Refaie
  • Rami Fouad
  • Raed Athamneh

Abstract

Thus far, the Taguchi technique is found only efficient in obtaining the combination of optimal factor settings when a single product/process response is considered. In today’s dynamic environment, customers are interested in multiple quality responses. This research, therefore, utilizes fuzzy logic and backward-propagation neural networks (BPNNs) to optimize process performance for products of multiple quality responses. In this research, quality characteristics are transformed to signal to noise (S/N) ratios, which are then used as inputs to a fuzzy model to obtain a single common output measure (COM). Next, BPNNs are employed to obtain full-factorial experimental data. Finally, the combination of factor levels that maximizes the average COM value is chosen as the optimal combination. Three case studies are provided for illustration; in all of which the proposed approach provided the largest total anticipated improvement. This indicates that the proposed approach is more efficient than Taguchi-fuzzy, grey-Taguchi, and Taguchi-utility methods. In conclusion, the fuzzy-BPNN approach may greatly assist process/product engineers in optimizing performance with multiple responses in a wide range of business applications.

Suggested Citation

  • Abbas Al-Refaie & Rami Fouad & Raed Athamneh, 2019. "Fuzzy Logic and Back-Propagation Neural Networks for Optimal Performance," Modern Applied Science, Canadian Center of Science and Education, vol. 13(2), pages 157-157, February.
    • Handle: RePEc:ibn:masjnl:v:13:y:2022:i:2:p:157
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    References listed on IDEAS

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    3. Abbas Al-Refaie, 2012. "Optimizing performance with multiple responses using cross-evaluation and aggressive formulation in data envelopment analysis," IISE Transactions, Taylor & Francis Journals, vol. 44(4), pages 262-276.
    4. Abbas Al-Refaie & Ibrahim Rawabdeh & Reema Abu-alhaj & Issam S. Jalham, 2012. "A Fuzzy Multiple Regression Approach for Optimizing Multiple Responses in the Taguchi Method," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 2(3), pages 13-34, July.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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