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

By Kevin L. Priddy

This instructional textual content offers the reader with an figuring out of man-made neural networks (ANNs) and their program, starting with the organic structures which impressed them, throughout the studying equipment which have been constructed and the knowledge assortment techniques, to the various methods ANNs are getting used today.

The fabric is gifted with no less than math (although the mathematical information are integrated within the appendices for readers), and with a greatest of hands-on event. All really expert phrases are integrated in a thesaurus. the result's a hugely readable textual content that would train the engineer the guiding ideas essential to use and follow man made neural networks.


- Preface
- Acknowledgments
- Introduction
- studying Methods
- information Normalization
- facts assortment, guidance, 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 numerous Neural Networks
- Appendix D: thesaurus of Terms
- References
- Index

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Extra info for Artificial Neural Networks: An Introduction

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The error signal is then used to modify the response of the learning system, adapting weights for neural networks, so that its response more closely matches that of the knowledge expert. The knowledge expert can be a human expert, a function, a set of rules, a set of measured system outputs, and so forth. The learning system can be trained by using any number of adaptation methods such as backpropagation, fuzzy logic, expert-system rules, evolutionary computation, statistical methods, or an ad hoc method.

In practice, neural-net designers often choose the total number of training samples to be 10 times as large as the VC dimension [Rogers, 1997]. 4 The Number of Hidden Layers Cybenko [Cybenko, 1989] demonstrated that a single hidden layer, given enough neurons, can form any mapping needed. In practice, two hidden layers are often used to speed up convergence. While some feedforward networks have been reported in the literature to contain as many as five or six hidden layers, the additional layers are not necessary.

5 Data Labeling For supervised approaches, the data must be labeled or truthed. This requires the neural-network designer, or a model, to assign target values to each sample collected. If the label is not already a number, then it must be converted to a numerical form in order for the neural network to be trained via computer. While samples do not generally need to be labeled for unsupervised approaches, the neural-network designer does need to know something about the data in order to interpret the results.

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