Neural Network Algorithms and Their Engineering Applications

Neural Network Algorithms and Their Engineering Applications book cover

Neural Network Algorithms and Their Engineering Applications

Author(s): Chao Huang Ph.D. (Author), Hailong Huang Ph.D. (Author), Yiying Zhang PhD (Author)

  • Publisher: Morgan Kaufmann
  • Publication Date: Jan. 28 2025
  • Language: English
  • Print length: 242 pages
  • ISBN-10: 0443292027
  • ISBN-13: 9780443292026

Book Description

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.

Editorial Reviews

Review

Complete guide to the latest advances in artificial neural network algorithms and their application across a wide range of engineering research

From the Back Cover

Optimization problems exist in nearly every engineering field. 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. The book provides readers with a wealth of source code covering all the applied algorithms. The code applications enhance readers' understanding of the methods covered in the book and also facilitate readers' ability to apply the algorithms to their own research and development projects.

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