Artificial Neural Networks for Engineering Applications
Published on 13. February 2019
Book
Paperback/Softback
176 pages
978-0-12-818247-5 (ISBN)
Description
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.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Product notice
Paperback (trade)
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 10 mm
Weight
315 gr
ISBN-13
978-0-12-818247-5 (9780128182475)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Alma Y Alanis | Nancy Arana-Daniel | Carlos Lopez-Franco
Artificial Neural Networks for Engineering Applications
E-Book
02/2019
Academic Press
€113.00
Available for download
Persons
Dr. Alma Y. Alanis received her M.Sc. and Ph.D. degrees in electrical engineering from the Advanced Studies and Research Center of the National Polytechnic Institute (CINVESTAV-IPN), Guadalajara, Mexico. Since 2008 she has been with University of Guadalajara, where she is currently a Dean of the Technologies for Cyber-Human Interaction Division, CUCEI. She is also member of the Mexican National Research System (SNI-2) and member of the Mexican Academy of Sciences. She has published papers in recognized International Journals and Conferences, besides eight international books. Dr. Alanis is a Senior Member of the IEEE and Subject Editor of the Journal of Franklin Institute, Section Editor at Open Franklin, Technical Editor at ASME/IEEE Transactions on Mechatronics, and Associate Editor at IEEE Transactions on Cybernetics, Intelligent Automation & Soft Computing and Engeenering Applications of Artifical Intelligence. Moreover, Dr. Alanis is currently serving on a number of IEEE and IFAC Conference Organizing Committees. In 2013 Dr. Alanis received the grant for women in science by L'Oreal-UNESCO-AMC-CONACYT-CONALMEX. In 2015, she received the Marcos Moshinsky Research Award. Her research interest centers on artificial neural networks, learning systems, intelligent control, and intelligent systems. Nancy Arana-Daniel received her B. Sc. Degree from the University of Guadalajara in 2000, and her M. Sc. And Ph.D. degrees in electric engineering with the special field in computer sicence from Research Center of the National Polytechnic Institute and Advanced Studies, CINVESTAV, in 2003 and 2007 respectively. She is currently a research fellow at the University of Guadalajara, in the Department of Computer Science Mxico, where she is working at the Laboratory of Intelligent Systems and the Research Center for Control Systems and Artificial Intelligence. She is IEEE Senior member and a member of National System of Researchers (SNI-1). She has published several papers in International Journals and Conferences and she has been technical manager of several projects that have been granted by the Nacional Council of Science and Technology (CONACYT). Also, se has collaborated in an international project granted by OPTREAT), She is Associated Editor of the Journal of Franklin Institute (Elsevier). Her research interests focus on applications of geometric algebra, geometric computing, machine learning, bio-inspired optimization, pattern recognition and robot navigation. Carlos Lpez-Franco received the Ph.D. degree in Computer Science in 2007 from the Center of Research and Advanced Studies, CINVESTAV, Mexico. He is currently a professor at the University of Guadalajara, Mexico, Computer Science Department, and member of the Intelligent Systems group. He is IEEE Senior member and a member of National System of Researchers) or SNI, level 1. His research interests include geometric algebra, computer vision, robotics and intelligent systems.
Content
1. Hierarchical Dynamic Neural Networks for Cascade System Modeling with Application to Wastewater Treatment
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
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