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

By Kevin L. Priddy

ISBN-10: 0819459879

ISBN-13: 9780819459879

This instructional textual content presents the reader with an knowing of man-made neural networks (ANNs) and their software, starting with the organic platforms which galvanized them, in the course of the studying tools which have been constructed and the information assortment strategies, to the various methods ANNs are getting used today.

The fabric is gifted with at the least math (although the mathematical information 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 train the engineer the guiding rules essential to use and practice synthetic neural networks.


- Preface
- Acknowledgments
- Introduction
- studying Methods
- facts Normalization
- information assortment, instruction, 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: characteristic Saliency
- Appendix C: Matlab Code for varied Neural Networks
- Appendix D: word list of Terms
- References
- Index

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S. companies. Each stock in the index is selected for liquidity, size, and industry and is weighted for market capitalization. Using the S&P 500 feature rather than each of the individual stocks to track the market is a way to reduce the total number of individual stocks that need to be tracked, while retaining important trend information. Thus, the S&P 500 index is an extracted feature that uses domain-specific knowledge. For some applications, an extracted feature can provide all of the information the neural network needs.

5. The mean and standard deviation are computed for each feature and used in the transformation given in Eq. 3) with a logistic sigmoid. It puts the normalized data in a range of 0 to 1. 3) . 4) Data Normalization 17 This transformation is almost linear near the mean value and has a smooth nonlinearity at both extremes to ensure that all values are within a limited range. This maintains the resolution of most values that are within a standard deviation of the mean. Since most values are nearly linearly transformed, it is sometimes called Softmax normalization.

1 Example output coding scheme for a four-tree classifier. 9 32 Chapter 5 scaled back from the extreme edges of the neuron’s output range in order to prevent multiple inputs from receiving the same output value, a condition known as data squashing. , sigmoidal function). Another option is to use a linear activation function for the output neuron. 1) represents the rescaling of a target output, t, to the neuron’s output range and through the neuron’s activation function, ƒ. t = (maxtarget − mintarget ) · f t − minvalue maxvalue − minvalue + mintarget .

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