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,…mehr
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.
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.
Inhaltsangabe
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
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
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