Artificial Neural Networks: An Introduction by Kevin L. Priddy

By Kevin L. Priddy

This instructional textual content presents the reader with an realizing of man-made neural networks (ANNs) and their software, starting with the organic structures which galvanized them, in the course of the studying equipment which have been built and the knowledge assortment techniques, to the numerous methods ANNs are getting used today.

The fabric is gifted with no less than math (although the mathematical info are incorporated within the appendices for readers), and with a greatest of hands-on adventure. All really expert phrases are integrated in a word list. the result's a hugely readable textual content that might educate the engineer the guiding rules essential to use and follow man made neural networks.

Contents

- Preface
- Acknowledgments
- Introduction
- studying Methods
- info Normalization
- facts assortment, education, Labeling, and enter Coding
- Output Coding
- Post-Processing
- Supervised education Methods
- Unsupervised education Methods
- Recurrent Neural Networks
- A Plethora of Applications
- facing constrained quantities of Data
- Appendix A: The Feedforward Neural Network
- Appendix B: function Saliency
- Appendix C: Matlab Code for varied Neural Networks
- Appendix D: word list of Terms
- References
- Index

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Extra info for Artificial Neural Networks: An Introduction

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When applied properly, feature selection and extraction preserves the information necessary for training while reducing the number of features. However, blindly applying feature reduction can lead to poor performance, and the process of selecting and extracting a set of features to produce a reduced number of inputs can require application-specific domain knowledge. The extracted or retained features should preserve class separation in classifiers, estimation accuracy for estimators, and groupings in self-organizers.

1, the data are overlapped, which makes pattern recognition between the classes difficult. The PCA transformation helps to minimize the overlap between data classes, as shown in Fig. 3. By projecting the data onto the eigenvectors, sorted largest to smallest, of the covariance matrix of the original data, a new set of features is obtained that provides the best separation for the data, starting with the maximum variance down to the smallest. As with the other normalization techniques, once the desired eigenvectors are found, all data presented to the neural network will need to be projected into features in the PCA domain before the proper output is obtained from the network.

The value used to threshold the output can be adjusted to produce the optimum ratio of detection to false alarms. Sometimes, it is useful to have an upper and lower threshold for a given classifier design, permitting the classifier to have a “not sure” or indeterminate region. If an output falls above the upper threshold, it is marked as part of the class. If it falls below the lower threshold, it is marked as not part of the class. If it falls between the two thresholds, then the class should be considered indeterminate.

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