List of Articles Adaptive Neural Fuzzy Inference System (ANFIS)

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  1 - Cycle of supplier development and segmentation using ANFIS
  Mansour Esmaeilzadeh Laya Olfat Maghsoud Amiri Iman Raeesi Vanani
  10.30495/imj.2023.1961109.1778
  Manufacturers segment their strategic suppliers for their development. Supplier segmentation creates value and synergy with the supplier. purpose of this article is to link supplier development and supplier segmentation using ANFIS in automotive industry. For this purpo More

  Manufacturers segment their strategic suppliers for their development. Supplier segmentation creates value and synergy with the supplier. purpose of this article is to link supplier development and supplier segmentation using ANFIS in automotive industry. For this purpose, first for each category, using three methods three fuzzy-neural systems were designed and using six methods (two classical methods and four meta-heuristic algorithms were optimized and the most suitable Neural Fuzzy system was selected. Then, the development score for 53 suppliers from the strategic suppliers of the automotive industry was predicted in four categories. according to the obtained scores, the suppliers were segmented. Finally, based on the activities related to the development of different capabilities, the necessary activities were proposed for the development of each of the suppliers. results show that automotive industry manufacturers at the supplier level must prioritize the use of activities related to relationship development, tangible capabilities, intangible capabilities, and environmental capabilities. Due to the fact that few studies have been done in this field, in the end, while comparing this study with one of the foreign studies, suggestions for the Iranian automotive industry as well as future research are presented. Manuscript profile
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  2 - Improving the Performance of Adaptive Neural Fuzzy Inference System (ANFIS) Using a New Meta-Heuristic Algorithm
  Mehdi Khadem Abbas Toloie Eshlaghy Kiamars Fathi hafshejani
  10.30495/ijm2c.2023.1955549.1251
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