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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| Κύριοι συγγραφείς: | , , |
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| Συγγραφή απο Οργανισμό/Αρχή: | |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | Αγγλικά |
| Έκδοση: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
|
| Έκδοση: | 1st ed. 2025. |
| Σειρά: | Studies in Systems, Decision and Control,
588 |
| Θέματα: | |
| Διαθέσιμο Online: | https://doi.org/10.1007/978-3-031-88091-9 |
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| 245 | 1 | 0 | |a Fractional-Order Activation Functions for Neural Networks |h [electronic resource] : |b Case Studies on Forecasting Wind Turbines' Generated Power / |c by Kishore Bingi, Ramadevi Bhukya, Venkata Ramana Kasi. |
| 250 | |a 1st ed. 2025. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2025. | |
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| 490 | 1 | |a Studies in Systems, Decision and Control, |x 2198-4190 ; |v 588 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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 to learn complex patterns in data. However, traditional activation functions have limitations such as non-differentiability, vanishing gradient problems, and inactive neurons at negative inputs, which can affect the performance of neural networks, especially for tasks involving intricate nonlinear dynamics. To address these issues, fractional-order derivatives from fractional calculus have been proposed. These derivatives can model complex systems with non-local or non-Markovian behavior. The aim is to improve wind power prediction accuracy using datasets from the Texas wind turbine and Jeju Island wind farm under various scenarios. The book explores the advantages of fractional-order activation functions in terms of robustness, faster convergence, and greater flexibility in hyper-parameter tuning. It includes a comparative analysis of single and multi-layer fractional-order neural networks versus conventional neural networks, assessing their performance based on metrics such as mean square error and coefficient of determination. The impact of using machine learning models to impute missing data on the performance of networks is also discussed. This book demonstrates the potential of fractional-order activation functions to enhance neural network models, particularly in predicting chaotic time series. The findings suggest that fractional-order activation functions can significantly improve accuracy and performance, emphasizing the importance of advancing activation function design in neural network analysis. Additionally, the book is a valuable teaching and learning resource for undergraduate and postgraduate students conducting research in this field. . | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Electric power production. | |
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| 700 | 1 | |a Bhukya, Ramadevi. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Kasi, Venkata Ramana. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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