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Genetic Algorithm for Neurocomputer Image Recognition

  • Conference paper
Artificial Neural Nets and Genetic Algorithms

Abstract

Genetic algorithms for optimization of feature set and internal structure of neural networks are considered. Results of experimental investigation of genetic algorithms are given. Experiments show that performance of neural networks after such optimization substantially increases.

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References

  1. Fukushima, K. Applied Optics, 26(23), 4985, (1987).

    Article  Google Scholar 

  2. Hecht-Nielsen, R. Neurocomputing. Addison-Wesley, Reading, MA, 1990.

    Google Scholar 

  3. Kussul, E.M., Luk, A.N. Soviet science review. Scientific developments in the USSR. 3 (3), 168 (1972).

    Google Scholar 

  4. Clopf, A.H., Gose, E.E. IEEE Trans. Syst. Sci. and Cybernet., 5 (3), 247 (1969).

    Article  Google Scholar 

  5. Mucciardy, A.N., Gose, E.E. IEEE Trans. Electron. Computers, EC-15 (N2), 257 (1966).

    Article  Google Scholar 

  6. Kussul, E.M., Baidyk, T.N., Lukovitch, V.V., Rachkovskij, D.A. Adaptive high performance classifier based on random threshold neurons. Proc. of Twelfth European Meeting on Cybernetics and Systems Research (EMCSR-94), Austria, Vienna, April 5–8, 1994 in R. Trappl (ed.): Cybernetics and Systems’94, World Scientific Publishing Co.Pte.Ltd, Singapore. - P. 1687–1695.

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  7. Kussul, E.M., Baidyk, T.N. Neural Random Threshold Classifier in OCR Application. Proc. of the Second All-Ukrainian Intern. Conf.“UkrO- BRAZ’94”, Kyjiv, Ukraine, December 20–24, 1994. - P. 154–157.

    Google Scholar 

  8. Kussul, E.M., Rachkovskij, D.A., Baidyk, T.N. Associative-projective neural networks: architecture, implementation, applications. Proc. of Fourth Intern. Conf. “Neural Networks & their Applications”, Nimes, France, Nov.4–8 1991. - P. 463–476.

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© 1995 Springer-Verlag/Wien

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Kussul, E.M., Baidyk, T.N. (1995). Genetic Algorithm for Neurocomputer Image Recognition. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_33

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  • DOI: https://doi.org/10.1007/978-3-7091-7535-4_33

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-82692-8

  • Online ISBN: 978-3-7091-7535-4

  • eBook Packages: Springer Book Archive

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