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Total Size:
8.1 MB
Info Hash:
1D6A5DC12A38D48A25100A7111079EC9B5F8938A
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Added:
April 20, 2026, 6:54 a.m.
Stats:
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(Last updated: April 20, 2026, 6:58 a.m.)
| File | Size |
|---|---|
| Chao H. Neural Network Algorithms and Their Engineering Applications 2024.pdf | 8.1 MB |
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199.0 MB
[82
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14]
2023-06-02
| Uploaded by CtrlSystem | Size 199.0 MB | Health [ 82 /14 ] | Added 2023-06-02 |
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5.1 GB
[156
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229]
2023-06-02
| Uploaded by Cristie | Size 5.1 GB | Health [ 156 /229 ] | Added 2023-06-02 |
NOTE
SOURCE: Chao H. Neural Network Algorithms and Their Engineering Applications 2024
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COVER
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MEDIAINFO
Textbook in PDF format Neural Network Algorithms and Their Engineering Applications presents the relevant techniques used to improve the global search ability of neural network algorithms in solving complex engineering problems with multimodal properties. The book provides readers with a complete study of how to use artificial neural networks to design a population-based metaheuristic algorithm, which in turn promotes the application of artificial neural networks in the field of engineering optimization. The authors provide a deep discussion for the potential application of Machine Learning methods in improving the optimization performance of the neural network algorithm, helping readers understand how to use Machine Learning methods to design improved versions of the algorithm. Users will find a wealth of source code that covers all applied algorithms. Code applications enhance readers' understanding of methods covered and facilitate readers' ability to apply the algorithms to their own research and development projects. The neural network algorithm is a new type of metaheuristic algorithm that has been proposed recently, and it is also the first metaheuristic algorithm with artficial neural networks as incentives. The neural network algorithm incorporates the properties of the metaheuristic algorithm with the feedback artficial neural network as the basic framework, and it shows excellent global search capabilities. However, the neural network algorithm converges slowly and has the risk of falling into the local optimal solution when solving complex multi-peak optimization problems. In view of this, we have been committed to improving the optimization performance of the neural network algorithm through different technologies in recent years to solve different types of complex engineering optimization problems. This book is a summary of our achievements from 2019 to 2024. As a result of reading this book, I hope that readers can understand the characteristics of neural networks and have a deep understanding of the improvement techniques of neural network algorithms. In addition, this book also includes the source code of the algorithms used. We hope that readers can try to use metaheuristic algorithms to solve complex optimization problems. Moreover, we also encourage readers to use neural network algorithms as an inspiration to try to design more metaheuristic algorithms based on artficial neural networks, to further strengthen the deep integration of artficial neural networks and metaheuristic algorithms and to provide more abundant algorithm options for solving complex engineering optimization problems. - Provides a comprehensive understanding of the development of metaheuristics, helping readers grasp the principle of employing artificial neural networks to design a population-based metaheuristic algorithm - Shows readers how to overcome the challenges faced in applying neural network algorithms to complex engineering optimization problems with multimodal properties - Demonstrates how to design new variants of neural network algorithms and how to apply Machine Learning methods to neural network algorithms - Covers source code to help readers solve engineering optimization problems - Shows readers how to develop the offered source code to create innovative solutions to their problems
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