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Elsevier

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

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.

Key features

  • 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

Readership

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.

Table of contents

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

Product details

  • Edition: 1
  • Latest edition
  • Published: February 7, 2019
  • Language: English

About the editors

AY

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.

Affiliations and expertise
Dean of Technologies for Cyber-Human Interaction Division (CUCEI), Universidad de Guadalajara, Mexico

NA

Nancy Arana-Daniel

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.
Affiliations and expertise
University of Guadalajara, Guadalajara, Jalisco, Mexico

CL

Carlos Lopez-Franco

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.
Affiliations and expertise
University of Guadalajara, Guadalajara, Jalisco, Mexico

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