Artificial Neural Networks: An Introduction (SPIE Tutorial Texts in Optical Engineering, Vol. TT68)

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