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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.

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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.

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"The book under review is quite unique, covering many important topics usually omitted from introductory courses on artificial neural networks, and as such it is a valuable reference. ... A major advantage of this volume is the interesting choice of examples used, most of which are not commonly considered in the artificial neural network literature." (Sandro Skansi, Mathematical Reviews, April, 2018)

"This book would be very good for advanced undergraduate students, first-year graduate students, or for anyone wishing to learn about neural networks on their own. It was originally published in Brazil in Portuguese. ... The exercises thoroughly test the readers´ understanding of the descriptive material. The practical examples address the training and use of the architecture in the chapter." (Anthony J. Duben, Computing Reviews, April, 2017)

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