Morphological neural networks with dendritic processing for pattern classification

Humberto Sossa; Fernando Arce; Erik Zamora; Elizabeth Guevara

doi:10.1007/978-3-319-77770-2_2

Morphological neural networks with dendritic processing for pattern classification

Humberto Sossa, Fernando Arce, Erik Zamora, Elizabeth Guevara

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

7 Scopus citations

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.

Original language	English
Title of host publication	Advanced Topics on Computer Vision, Control and Robotics in Mechatronics
Publisher	Springer International Publishing
Pages	27-47
Number of pages	21
ISBN (Electronic)	9783319777702
ISBN (Print)	9783319777696
DOIs	https://doi.org/10.1007/978-3-319-77770-2_2 https://doi.org/10.1007/978-3-319-77770-2_2
State	Published - 28 Apr 2018

Keywords

Artificial intelligence
Morphological neural networks with dendritic processing Pattern classification

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title = "Morphological neural networks with dendritic processing for pattern classification",

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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T1 - Morphological neural networks with dendritic processing for pattern classification

AU - Sossa, Humberto

AU - Arce, Fernando

AU - Zamora, Erik

AU - Guevara, Elizabeth

PY - 2018/4/28

Y1 - 2018/4/28

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Morphological neural networks with dendritic processing Pattern classification

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SP - 27

EP - 47

BT - Advanced Topics on Computer Vision, Control and Robotics in Mechatronics

PB - Springer International Publishing

ER -

Morphological neural networks with dendritic processing for pattern classification

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