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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
Biomedical Engineers and researchers in the fields of applied engineering. Postgraduate students and research students in Biomedical Engineering, as well as Engineers across disciplines who want to learn how to apply Artificial Neural Networks to their work.
- 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
- No. of pages:
- © Academic Press 2019
- 13th February 2019
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Alma Y. Alanis, was born in Durango, Durango, Mexico, in 1980. She received the B. Sc. degree from Instituto Tecnologico de Durango (ITD), Durango Campus, Durango, Durango, in 2002, the M.Sc. and the Ph.D. degrees in electrical engineering from the Advanced Studies and Research Center of the National Polytechnic Institute (CINVESTAV-IPN), Guadalajara Campus, Mexico, in 2004 and 2007, respectively. Since 2008 she has been with University of Guadalajara, where she is currently a Chair Professor in the Department of Computer Science. 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 four International Books. She is a Senior Member of the IEEE and Subject and Associated Editor of the Journal of Franklin Institute (Elsevier) and Intelligent Automation and Soft Computing (Taylor and Francis), moreover she is currently serving on a number of IEEE and IFAC Conference Organizing Committees. In 2013, she receives the grant for women in science by L’Oreal-UNESCOAMC- CONACYT-CONALMEX. In 2015, she receives the Research Award Marcos Moshinsky. Since 2008 she is member for the Accredited Assessors record RCEACONACYT, evaluating a wide range of national research projects, besides she has belonged to important project evaluation committees of national and international research projects. Her research interest centers on neural control, backstepping control, block control, and their applications to electrical machines, power systems and robotics.
University of Guadalajara Guadalajara, Jalisco, Mexico
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.
University of Guadalajara, Guadalajara, Jalisco, Mexico
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.
University of Guadalajara, Guadalajara, Jalisco, Mexico
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