Neural Network Algorithms and Their Engineering Applications
Book Details
Format
Paperback / Softback
ISBN-10
0443292027
ISBN-13
9780443292026
Publisher
Elsevier Science & Technology
Imprint
Morgan Kaufmann Publishers In
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Jan 14th, 2025
Print length
242 Pages
Weight
510 grams
Product Classification:
Medical bioinformaticsEnterprise softwareAlgorithms & data structuresArtificial intelligence
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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.
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 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.
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