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Two-Dimensional Fast Orthogonal Neural Networks

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Advances in Neural Networks – ISNN 2016 (ISNN 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9719))

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Abstract

A new method of learning fast two-dimensional orthogonal transformations is considered. Tunable orthogonal transformations are regarded as special neural networks. The learning takes a finite number of steps. The learning algorithm does not have the error feedback and is absolutely stable. The method is based on fractal filtering of signals and images. Linguistic models are used to determine the topology and structure of fast transformations. Examples are given.

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References

  1. Andrews, H.C., Caspari, K.L.: A general techniques for spectral analysis. IEEE. Tr. Comput. C19, 16–25 (1970)

    Article  MATH  Google Scholar 

  2. Solodovnikov, A.I., Spivakovskii, A.M.: Osnovy Teorii I Metody Spektralnoi Obrabotki Informatsii. Leningrad (1986). (in Russian)

    Google Scholar 

  3. Dorogov, A.Y.: Bystrye Neironnye Seti: Proektirovanie, Nastroika, Prilojenie. http://bookfi.org/book/805420. (in Russian)

  4. Good, I.J.: The interaction algorithm and practical fourier analysis. J. R. Statistic. Soc. Ser. B 20, 361–372 (1958)

    MathSciNet  MATH  Google Scholar 

  5. Dorogov, A.Y., Shestopalov, M.Y.: Neirosetevoe Modelirovanie Regulyarnyh Fraktalov, in Neirocompyutery: razrabotka I primenenie, no. 6 (2007). (in Russian)

    Google Scholar 

  6. Dorogov, A.Y.: Fractal learning of fast orthogonal neural networks. J. Opt. Mem. Neural Netw. (Information Optics) 21, 105–118 (2012)

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Acknowledgements

The paper was prepared in SPbETU and is supported by the Contract № 02.G25.31.0149 dated 01.12.2015 (Board of Education of Russia).

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Authors and Affiliations

  1. Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia

    A. Yu. Dorogov

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  1. A. Yu. Dorogov
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Correspondence to A. Yu. Dorogov .

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Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. Huazhong University of Science and Tech., Wuhan, Jiangsu, China

    Qingshan Liu

  3. Russian Academy of Sciences, SPIIRAS, St. Petersburg, Russia

    Andrey Ronzhin

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© 2016 Springer International Publishing Switzerland

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Dorogov, A.Y. (2016). Two-Dimensional Fast Orthogonal Neural Networks. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_24

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  • DOI: https://doi.org/10.1007/978-3-319-40663-3_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40662-6

  • Online ISBN: 978-3-319-40663-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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