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Decision Making01:20

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data.

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This study introduces a novel neural network approach for multi-objective decision-making problems. The method effectively handles limited fuzzy data, offering favorable results compared to existing techniques.

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Area of Science:

  • Artificial Intelligence
  • Computational Intelligence
  • Operations Research

Background:

  • Multi-objective decision-making (DM) problems are complex and often require efficient methods for utility evaluation.
  • Traditional approaches may struggle with limited or fuzzy data scenarios.
  • Neural networks (NNs) offer a powerful framework for modeling complex functions.

Purpose of the Study:

  • To propose a novel neural network-based approach for solving multi-objective decision-making problems.
  • To estimate the utility evaluation function using a Group Method of Data Handling (GMDH) neural network.
  • To demonstrate the effectiveness of the proposed method with limited fuzzy data.

Main Methods:

  • Utilizing a Group Method of Data Handling (GMDH) neural network for utility function estimation.
  • Training the neural network with data from a limited set of initial solutions.
  • Adjusting neural network parameters using error propagation and the Unscented Kalman Filter (UKF).

Main Results:

  • The proposed method effectively solves multi-objective decision-making problems with limited fuzzy data.
  • Favorable and effective results were achieved in a practical application.
  • Performance was validated through comparison with similar existing methods.

Conclusions:

  • The developed neural network approach provides an effective solution for multi-objective decision-making, especially under data limitations.
  • The combination of GMDH NN, error propagation, and UKF offers a robust framework.
  • The method shows significant potential for real-world applications requiring decision support with imprecise data.