Abstract
The generality of the artificial neural networks models infers the requests based in the totality of the characteristics of the patterns. The RHI model infers just with a limited set of this characteristics, the significant fragment. This reason make RHI really appropriated by resolution of control and active vision problem. Although RHI model present high sensibility to distortion. In this paper it is developed the formalism to obtain the significant fragment in such a way it improve the noise tolerance.
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García Chamizo, J.M., Satorre Cuerda, R., Ibarra Picó, F., Cuenca Asensi, S. (1995). Selecting the Best Significant Fragment to the Incremental Heteroassociative Neural Network (RHI). In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_49
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_49
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
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