Chao Huang, Hailong Huang, Yiying Zhang

Neural Network Algorithms and Their Engineering Applications (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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. - 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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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 200
• Erscheinungstermin: 9. Januar 2025
• Englisch
• ISBN-13: 9780443292033
• Artikelnr.: 72789965

Autorenporträt

Dr. Chao Huang received the B.Sc. degree in automation, from China University of Petroleum, Beijing, China, in June 2012, and received Ph.D degree from the University of Wollongong, Australia, in Dec. 2018. From Sep. 2018 to Sep. 2019, she worked as a project leader at National Institute of Informatics, Tokyo, Japan. From Oct. 2019 to July 2021, she worked as a postdoctoral research fellow at the School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore. She is now a research assistant professor at the Department of Industrial and Systems Engineering, the Hong Kong Polytechnic University, Hong Kong. Her interests include motion planning, human machine collaboration, fault tolerant, and automotive control and application.Dr. Hailong Huang received a B.Sc. degree in automation, from China University of Petroleum, Beijing, China, in June 2012, and received Ph.D degree in Systems and Control from the University of New South Wales, Sydney, Australia, in March 2018. From Feb. 2018 to July 2021, he worked as a postdoctoral research fellow at the School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, Australia. He is now an assistant professor at the Department of Aeronautical and Aviation Engineering, the Hong Kong Polytechnic University. His current research interests include the coordination, navigation and control of ground robots and unmanned aerial vehicles.Dr Yiying ZHANG received the B.S. degree from Yanshan University, Qinhuangdao, China, in 2014, the M.S. degree from Harbin Institute of Technology, Harbin, China, in 2016, and the Ph.D. degree from Tianjin University, Tianjin, China, in 2021, all in information and communication engineering. He is currently a postdoctoral fellow of the Hong Kong Polytechnic University. His research interests include machine learning, evolutionary computation, and path planning of unmanned aerial vehicles.

Inhaltsangabe

1. Introduction
2. Neural network algorithm
3. Constrained engineering design optimization problems
4. Parameter estimation of proton exchange membrane fuel cell models
5. Parameter extraction of photovoltaic models
6. Discussion on the applications of machine learning methods in neural
network algorithm
7. Conclusion