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. Hierarchical Dynamic Neural Networks for Cascade System Modeling with Application to Wastewater Treatment2. Hyperellipsoidal Neural Network trained with Extended Kalman Filter for forecasting of time series3. Neural networks: a methodology for modeling and control design of dynamical systems4. Continuous-Time Decentralized Neural Control of a Quadrotor UAV5. Support Vector Regression for digital video processing6. Artificial Neural Networks Based on Nonlinear Bioprocess Models for Predicting Wastewater Organic Compounds and Biofuels Production7. Neural Identification for Within-Host Infectious Disease Progression8. Attack Detection and Estimation for Cyber-physical Systems by using Learning Methodology9. Adaptive PID Controller using a Multilayer Perceptron Trained with the Extended Kalman Filter for an Unmanned Aerial Vehicle10. Sensitivity Analysis with Artificial Neural Networks for Operation of Photovoltaic Systems11. Pattern Classification and its Applications to Control of Biomechatronic Systems