Advanced Optimization Techniques: RSM vs ANN in Engineering Applications

  • الفئة :

      كتب علمية

  • الإسم الكامل للمؤلف (ين):

      Dr. Ghermoul Oussama, Dr. Benguesmia Hani

  • رقم الإيداع القانوني (ISBN) :

      978-9969-05-211-4

  • عدد الصفحات :

      86

  • الميدان :

      علوم وتكنولوجيا


: ملخص

This guide aims to enhance the predictive modeling and optimization skills of all students across various engineering disciplines, especially electrical engineering students (Master's and PhD students). It primarily explores advanced optimization techniques, focusing on RSM and ANN. The guide details RSM CCD for structured system optimization and delves into the predictive capabilities of ANN, including FNN, RNN, and RBFN. The author encourages reader feedback to improve the guide's relevance and usefulness for both students and professionals in engineering


  • الفهرس :

       
  • I. Response surface methodology
    I.1. Steps of the Central Composite Design
    I.2. Key Terms in Response Surface Methodology (RSM)
    I.3. The Alpha (α) Value
    I.4. Types of Design in CCD
    I.5. Types of models
    I.6. Analysis of Variance
    I.7. Surface and Contour Plots
    I.8. Insulator flashover voltage prediction
    I.9. Conclusion
    II. Artificial neural network
    II.1. Types of Artificial Neural Networks
    II.2. Structure of ANN
    II.3. Mathematical structure of ANN
    II.4. Types of Learning in an ANN
    II.5. Regularization Techniques
    II.6. Forward-Propagation
    II.7. Back-propagation
    II.8. Cost and Loss Functions in Neural Networks
    II.9. Cost Function
    II.10. Data Management and Splitting in ANN Training
    II.11. Insulator flashover voltage prediction
    II.12. Conclusion
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