Abstract
Morphological neural networks, in particular, those with dendritic processing (MNNDPs), have shown to be a very promising tool for pattern classification. In this chapter, we present a survey of the most recent advances concerning MNNDPs. We provide the basics of each model and training algorithm; in some cases, we present simple examples to facilitate the understanding of the material. In all cases, we compare the described models with some of the state-of-the-art counterparts to demonstrate the advantages and disadvantages. In the end, we present a summary and a series of conclusions and trends for present and further research.
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Acknowledgements
E. Zamora and H. Sossa would like to acknowledge UPIITA-IPN and CIC-IPN for the support to carry out this research. This work was economically supported by SIP-IPN (grant numbers 20160945, 20170836 and 20161116, 20170693, 20180730, 20180180), and CONACYT (grant number 155014 (Basic Research) and grant number 65 (Frontiers of Science). F. Arce acknowledges CONACYT for the scholarship granted toward pursuing his PhD studies.
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Sossa, H., Arce, F., Zamora, E., Guevara, E. (2018). Morphological Neural Networks with Dendritic Processing for Pattern Classification. In: Vergara Villegas, O., Nandayapa , M., Soto , I. (eds) Advanced Topics on Computer Vision, Control and Robotics in Mechatronics. Springer, Cham. https://doi.org/10.1007/978-3-319-77770-2_2
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