Artificial Neural Networks for Engineering Applications
- 1st Edition - February 7, 2019
- Latest edition
- Editors: Alma Y Alanis, Nancy Arana-Daniel, Carlos Lopez-Franco
- Language: English
Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that cannot be solved through conventio… Read more
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Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that cannot be solved through conventional methods. The proposed methodologies can be applied to modeling, pattern recognition, classification, forecasting, estimation, and more. Readers will find different methodologies to solve various problems, including complex nonlinear systems, cellular computational networks, waste water treatment, attack detection on cyber-physical systems, control of UAVs, biomechanical and biomedical systems, time series forecasting, biofuels, and more. Besides the real-time implementations, the book contains all the theory required to use the proposed methodologies for different applications.
- Presents the current trends for the solution of complex engineering problems that cannot be solved through conventional methods
- Includes real-life scenarios where a wide range of artificial neural network architectures can be used to solve the problems encountered in engineering
- Contains all the theory required to use the proposed methodologies for different applications
2. Hyperellipsoidal Neural Network trained with Extended Kalman Filter for forecasting of time series
3. Neural networks: a methodology for modeling and control design of dynamical systems
4. Continuous–Time Decentralized Neural Control of a Quadrotor UAV
5. Support Vector Regression for digital video processing
6. Artificial Neural Networks Based on Nonlinear Bioprocess Models for Predicting Wastewater Organic Compounds and Biofuels Production
7. Neural Identification for Within-Host Infectious Disease Progression
8. Attack Detection and Estimation for Cyber-physical Systems by using Learning Methodology
9. Adaptive PID Controller using a Multilayer Perceptron Trained with the Extended Kalman Filter for an Unmanned Aerial Vehicle
10. Sensitivity Analysis with Artificial Neural Networks for Operation of Photovoltaic Systems
11. Pattern Classification and its Applications to Control of Biomechatronic Systems
- Edition: 1
- Latest edition
- Published: February 7, 2019
- Language: English
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Alma Y Alanis
Alma Y. Alanis received a Ph.D. degree in electrical engineering from the Advanced Studies and Research Center of the National Polytechnic Institute (CINVESTAV-IPN), Guadalajara Campus, Mexico, in 2007. Since 2008, she has been with the University of Guadalajara, where she is currently a Chair Professor in the Department of Computer Science. She is also a member of the Mexican National Research System (SNI-3) and the Mexican Academy of Sciences. She has published papers in recognized international journals and conferences, as well as eight international books. She is a Senior Member of the IEEE and a Subject Editor for the Journal of Franklin Institute (Elsevier), IEEE/ASME Transactions on Mechatronics, IEEE Access, IEEE Latin American Transactions, and Intelligent Automation & Soft Computing. In 2013, she received the grant for women in science by L'Oreal-UNESCO-AMC-CONACYT-CONALMEX. In 2015, she received the Marcos Moshinsky Research Award. Since 2008, she has been a member of the Accredited Assessors record RCEA-CONACYT, evaluating a wide range of national research projects, and has served on important national and international project evaluation committees. Her research interests center on neural control, backstepping control, block control, and their applications to electrical machines, power systems, and robotics.
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Nancy Arana-Daniel
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