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.

This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.