Download Artificial Neural Networks: An Introduction by Kevin L. Priddy PDF

By Kevin L. Priddy

This educational 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, throughout the studying equipment which have been constructed and the information assortment strategies, to the numerous methods ANNs are getting used today.

The fabric is gifted with at least math (although the mathematical info are integrated within the appendices for readers), and with a greatest of hands-on adventure. All really expert phrases are incorporated in a thesaurus. the result's a hugely readable textual content that might educate the engineer the guiding rules essential to use and follow synthetic neural networks.

Contents

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

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Extra info for Artificial Neural Networks: An Introduction

Sample text

Unlike the feedforward neural networks the reader has been introduced to, in the SOM, the weights leading to each neuron are trained to place items with similar characteristics, such as cars or planes, together, yet provide separation for items that are not alike. SOMs are unique in that they construct topology-preserving mappings of the training data where the location of a neuron encodes semantic information. One of the main applications for SOMs is clustering data for display as a two-dimensional image so that the data are easy to visualize.

Feature vectors can contain features that are correlated with one another, which Data Collection, Preparation, Labeling, and Input Coding 27 represents redundant information. 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.

This is something that the reader should always remember! 2 Fisher iris data plotted for first three features. 3 Fisher iris data after PCA transformation using projection of the original data onto the first three eigenvectors of the covariance matrix as features. 20 Chapter 3 the majority of their time developing good robust features. Then, the selection of the classifier is a matter of choice based upon system constraints. The eigenvectors obtained from the PCA process for the Fisher iris data are given in Fig.

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