Artificial Neural Networks: A Practical Course

Introduction.- PART I - Neural Networks Architectures and Their Theoretical Aspects.- Architectures of Artificial Neural Networks and Training Processes.- Perceptron Network and Learning Rule.- Adaline Network and Delta Rule.- Multilayer Perceptron (MLP).- Radial Basis Function (RBF).- Recurrent Neural Topologies and Hopfield Network.- Self-Organizing Maps and Kohonen Network.- Learning Vector Quantization (LVQ) and Counter-Propagation Network.- Adaptive Resonance Theory (ART).- Part II - Artificial Neural Networks Applications in Problems of Engineering and Applied Sciences.- Coffee Global Quality Estimation Using Multilayer Perceptron.- Computer Network Traffic Analysis Using SNMP Protocol and LVQ Network.- Forecasting Stock Market Trends Using Recurrent Network.- System for Disease Diagnosis Using ART Network.- Adulterants Patterns Identification in Coffee Powder Using Self-Organizing Maps.- Disturbances Recognition Related to Electrical Power Quality Using PMC Network.- Mobile Robot Trajectory Control Using Fuzzy System and MLP Network.- Method to Tomatoes Classification Using Computer Vision and MLP Network.- Analysis of RBF and MLP Network Performance in Pattern Classification Problems.- Solving Constrained Optimization Problems Using Hopfield Network.- Conclusion.