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.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. List of Contributors - p. v 6. Dedication - p. vii 7. About the Editors - p. ix 8. Preface - p. xi 9. Acknowledgment - p. xiii 10. Chapter 1: Hierarchical Dynamic Neural Networks for Cascade System Modeling With Application to Wastewater Treatment - p. xiii 11. Chapter 2: Hyperellipsoidal Neural Network Trained With Extended Kalman Filter for Forecasting of Time Series - p. 8 12. Chapter 3: Neural Networks: A Methodology for Modeling and Control Design of Dynamical Systems - p. 19 13. Chapter 4: Continuous-Time Decentralized Neural Control of a Quadrotor UAV - p. 38 14. Chapter 5: Adaptive PID Controller Using a Multilayer Perceptron Trained With the Extended Kalman Filter for an Unmanned Aerial Vehicle - p. 53 15. Chapter 6: Support Vector Regression for Digital Video Processing - p. 63 16. Chapter 7: Artificial Neural Networks Based on Nonlinear Bioprocess Models for Predicting Wastewater Organic Compounds and Biofuel Production - p. 77 17. Chapter 8: Learning-Based Identification of Viral Infection Dynamics - p. 96 18. Chapter 9: Attack Detection and Estimation for Cyber-Physical Systems by Using Learning Methodology - p. 105 19. Chapter 10: Sensitivity Analysis With Artificial Neural Networks for Operation of Photovoltaic Systems - p. 126 20. Chapter 11: Pattern Classification and Its Applications to Control of Biomechatronic Systems - p. 138 21. Index - p. 155
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.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. List of Contributors - p. v 6. Dedication - p. vii 7. About the Editors - p. ix 8. Preface - p. xi 9. Acknowledgment - p. xiii 10. Chapter 1: Hierarchical Dynamic Neural Networks for Cascade System Modeling With Application to Wastewater Treatment - p. xiii 11. Chapter 2: Hyperellipsoidal Neural Network Trained With Extended Kalman Filter for Forecasting of Time Series - p. 8 12. Chapter 3: Neural Networks: A Methodology for Modeling and Control Design of Dynamical Systems - p. 19 13. Chapter 4: Continuous-Time Decentralized Neural Control of a Quadrotor UAV - p. 38 14. Chapter 5: Adaptive PID Controller Using a Multilayer Perceptron Trained With the Extended Kalman Filter for an Unmanned Aerial Vehicle - p. 53 15. Chapter 6: Support Vector Regression for Digital Video Processing - p. 63 16. Chapter 7: Artificial Neural Networks Based on Nonlinear Bioprocess Models for Predicting Wastewater Organic Compounds and Biofuel Production - p. 77 17. Chapter 8: Learning-Based Identification of Viral Infection Dynamics - p. 96 18. Chapter 9: Attack Detection and Estimation for Cyber-Physical Systems by Using Learning Methodology - p. 105 19. Chapter 10: Sensitivity Analysis With Artificial Neural Networks for Operation of Photovoltaic Systems - p. 126 20. Chapter 11: Pattern Classification and Its Applications to Control of Biomechatronic Systems - p. 138 21. Index - p. 155
