A Fuzzy Logic-Based Approach for Selecting the Optimal Crusher Model

@article{1705128,
      author = {Ogeleka Stephen Chike, Ebenezer Oyedele Ajaka},
      title = {A Fuzzy Logic-Based Approach for Selecting the Optimal Crusher Model},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {4},
      pages = {281-283},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705128.pdf},
      abstract = {This scientific journal presents a fuzzy logic-based approach for selecting the optimal model that minimizes the power rating, given the product size, feed size, and capacity. The study utilizes the `skfuzzy` library in Python to implement the fuzzy logic system. A dataset containing various model samples along with their corresponding feed size, product size, capacity, and power rating is loaded from a CSV file. Fuzzy membership functions are defined for the input variables: feed size, product size, and capacity, as well as the output variable: power rating. Fuzzy rules are established to determine the relationship between the input and output variables. The fuzzy control system is created and simulated to evaluate the power rating for each data sample. The model with the lowest power rating is identified as the optimal },
      keywords = {fuzzy logic, optimal model, feed size, product size},
      month = {October},
  }