By Kevin L. Priddy
This educational textual content presents the reader with an figuring out of man-made neural networks (ANNs) and their program, starting with the organic structures which impressed them, during the studying tools which were constructed and the knowledge assortment techniques, to the numerous methods ANNs are getting used today.
The fabric is gifted with at least 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 incorporated in a thesaurus. the result's a hugely readable textual content that may train the engineer the guiding ideas essential to use and observe man made neural networks.
Contents
- Preface
- Acknowledgments
- Introduction
- studying Methods
- information Normalization
- facts assortment, training, 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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Additional resources for Artificial Neural Networks: An Introduction
Example text
Data Collection, Preparation, Labeling, and Input Coding 23 why these outliers occurred. Outliers should be investigated thoroughly, because they might represent an important condition the designer had not considered. But, if the designer suspects a feature vector is erroneous, it should be removed from the data set. If the data contain a large number of outliers, the designer should suspect the entire data-collection process is flawed, and identify and correct the problem before starting over.
In order to use the backpropagation training method, the transfer function must be differentiable. The most common transfer functions are the logistic sigmoid, the hyperbolic tangent, the Gaussian, and the linear transfer function. In general, linear neurons require very small learning rates in order to train properly. Gaussian transfer functions are employed in radial basis function networks, often used to perform function approximation. For classification problems, nonlinear transfer functions work best.
Oftentimes, the desired features are problem domain dependent. For example, if you are doing vibration analysis on rotating machinery, it is a natural extension to transform data from the time domain to the frequency domain to improve performance. 4 First three eigenvectors for Fisher iris data. 5 Feedforward neural network implementing PCA for the Fisher iris data set. Chapter 4 Data Collection, Preparation, Labeling, and Input Coding Since neural networks are data driven, the adage “garbage in, garbage out” is highly relevant to the task of building a neural network.
