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.
- 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
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
