This tutorial text provides the reader with an understanding of artificial neural networks (ANNs) and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed and the data collection processes, to the many ways ANNs are being used today.
The material is presented with a minimum of math (although the mathematical details are included in the appendices for interested readers), and with a maximum of hands-on experience. All specialized terms are included in a glossary. The result is a highly readable text that will teach the engineer the guiding principles necessary to use and apply artificial neural networks. Contents: - Preface - Acknowledgments - Introduction - Learning Methods - Data Normalization - Data Collection, Preparation, Labeling, and Input Coding - Output Coding - Post-Processing - Supervised Training Methods - Unsupervised Training Methods - Recurrent Neural Networks - A Plethora of Applications - Dealing with Limited Amounts of Data - Appendix A: The Feedforward Neural Network - Appendix B: Feature Saliency - Appendix C: Matlab Code for Various Neural Networks - Appendix D: Glossary of Terms - References - Index.