Abstract
This chapter introduces the basic concepts and notation of unsupervised learning neural networks. Unsupervised networks are useful for analyzing data without having the desired outputs; in this case, the neural networks evolve to capture density characteristics of a data phase. We will describe in some detail competitive learning networks, Kohonen self-organizing networks, learning vector quantization, and Hopfield networks. We will also show some examples of these networks to illustrate their possible application in solving real-world problems.
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© 2003 Physica- Verlag Heidelberg
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Castillo, O., Melin, P. (2003). Unsupervised Learning Neural Networks. In: Soft Computing and Fractal Theory for Intelligent Manufacturing. Studies in Fuzziness and Soft Computing, vol 117. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1766-9_5
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DOI: https://doi.org/10.1007/978-3-7908-1766-9_5
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-662-00296-4
Online ISBN: 978-3-7908-1766-9
eBook Packages: Springer Book Archive