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Fractional-Order Activation Functions for Neural Networks Case Studies on Forecasting Wind Turbines' Generated Power /

This book suggests the development of single and multi-layer fractional-order neural networks that incorporate fractional-order activation functions derived using fractional-order derivatives. Activation functions are essential in neural networks as they introduce nonlinearity, enabling the models t...

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Detalles Bibliográficos
Main Authors: Bingi, Kishore (Author), Bhukya, Ramadevi (Author), Kasi, Venkata Ramana (Author)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:inglés
Publicado: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Edición:1st ed. 2025.
Series:Studies in Systems, Decision and Control, 588
Subjects:
Electric power production.
Engineering mathematics.
Engineering > Data processing.
Production engineering.
Mechanical Power Engineering.
Mathematical and Computational Engineering Applications.
Process Engineering.
Acceso en liña:https://doi.org/10.1007/978-3-031-88091-9
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Table of Contents:
  • Introduction
  • Fractional-order Activation Functions
  • Fractional-order Neural Networks
  • Forecasting of Texas Wind Turbines’ Generated Power
  • Forecasting of Jeju Islands Wind Turbines’ Generated Power
  • Forecasting of Renewable Energy Using Fractional-Order Neural Networks
  • Fractional Feedforward Neural Network-Based Smart Grid Stability Prediction Model.

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