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Artificial Neural Networks: An Introduction by Kevin L. Priddy

By Kevin L. Priddy

This instructional textual content offers the reader with an figuring out of synthetic neural networks (ANNs) and their program, starting with the organic structures which impressed them, during the studying tools which have been built and the information assortment tactics, to the various 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 event. All really good phrases are integrated in a thesaurus. the result's a hugely readable textual content that might educate the engineer the guiding ideas essential to use and practice man made neural networks.

Contents

- Preface
- Acknowledgments
- Introduction
- studying Methods
- info Normalization
- information assortment, guidance, 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: characteristic Saliency
- Appendix C: Matlab Code for varied Neural Networks
- Appendix D: thesaurus of Terms
- References
- Index

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This approach should give the best generalization. The weights generated beyond this point are more likely to fit the idiosyncrasies of the training data, and will not interpolate or generalize well. 4, we discussed rules of thumb for estimating the number of hidden neurons necessary to solve the problem. An empirical approach is to retrain the network with varying numbers of hidden neurons and observe the output error as a function of the number of hidden neurons. 13 shows the result of one network repetitively trained with different numbers of hidden neurons.

When the min-max normalization is applied, each feature will lie within the new range of values; but the underlying distributions of the corresponding features within the new range of values will remain the same. This allows the designer more flexibility in designing neural networks and in determining which features are of more worth than others when making a decision. Min-max normalization has the advantage of preserving exactly all relationships in the data, and it does not introduce any bias.

Ensure the data are protected from unauthorized access. , personal, strictly private, proprietary, business-sensitive, confidential, official-use-only, or classified data). If necessary, the data-collection plan should include appropriate procedures to protect the data and its intended application from unauthorized access. For example, medical technologies under development often require human subjects’ boards to show how patient information was protected from unauthorized access. ) from the data used during the training and testing process, limiting access to the data, and destroying all data records at the completion of the effort.

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