Bio-inspired techniques and their application to precision agriculture (Andres Perez-Uribe / Eduardo Sanchez)
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This document discusses bio-inspired techniques and their application to precision agriculture. It describes bio-inspired computational techniques that are capable of producing complex models to predict and describe site-specific crop behavior. These techniques include artificial intelligence, expert systems, computational intelligence techniques like artificial neural networks, evolutionary computation and fuzzy systems, as well as machine learning. The philosophy of bio-inspired systems is to study nature's inventions, tricks and artifices to develop engineering solutions with life-like properties.
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Hierarchical Self-Organizing Networks are used to reveal the topology and structure of datasets. Those structures create crisp partitions of the dataset producing branches or prototype vectors that represent groups of data with similar characteristics. However, when observations can be represented by several prototypes with similar accuracy, crisp partitions are forced to classify it in just one group, so crisp divisions usually lose information about the real dataset structure. To deal with this challenge we propose the Fuzzy Growing Hierarchical Self-Organizing Networks (FGHSON). FGHSON are adaptive networks which are able to reflect the underlying structure of the dataset, in a hierarchical fuzzy way. These networks grow by using three variables which govern the membership degree of data observations to its prototype vectors and the quality of the network representation. The resulting structure allows to represent heterogeneous groups and those that present similar membership degree to several clusters
Improving the correlation hunting in a large quantity of SOM component planes
The document discusses using self-organizing maps (SOMs) to analyze high-dimensional agro-ecological data related to sugar cane productivity. It presents a methodology to improve analyzing SOM component planes by organizing similar planes to aid in correlation hunting. The methodology uses a new SOM to cluster component planes based on distances, representing clusters as a tree structure. This allows finding relationships between variables like radiation and productivity for sugar cane culture.
Classification of similar productivity zones in the sugar cane culture using ...
This document discusses using self-organizing maps (SOMs) to cluster and analyze agro-ecological zones based on variables like climate, soil, and management factors to better understand sugar cane productivity. SOM component planes representing different variables are clustered based on distances in the SOM. This allows visualization of similar productivity zones and identification of relationships between variables like higher radiation before harvest being linked to increased sugar accumulation and medium-high productivity.
Mapas de Kohonen como una herramienta visual de apoyo al soporte de decisione...
Este documento describe el uso de mapas autoorganizados de Kohonen como una herramienta de visualización para apoyar la toma de decisiones en agroecología, con un estudio de caso sobre la caña de azúcar. Los mapas autoorganizados permiten reducir la dimensionalidad de los datos para facilitar su visualización y encontrar patrones entre variables como temperatura, precipitación y rendimiento de la caña. El documento presenta varios experimentos utilizando datos ambientales, de suelo y de manejo de la caña, mostrando correlaciones encontradas entre variables como la radi
Curso 2006 Sesion 1 Kohonen
Este documento describe los mapas autoorganizados o mapas de Kohonen, que son una herramienta para la visualización y análisis de datos multidimensionales. Explica la inspiración biológica detrás de los mapas autoorganizados, su arquitectura y algoritmo de aprendizaje no supervisado. El objetivo es agrupar datos similares y reducir la dimensionalidad para facilitar la visualización e interpretación de grandes conjuntos de datos.
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Algortimos bio-inspirados para clustering y visualizacion de datos geoespaciales
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Bio inspired computational techniques applied to the analysis and visualizati...
Bio inspired computational techniques applied to the analysis and visualizati...
Fuzzy Growing Hierarchical Self-organizing Networks
Fuzzy Growing Hierarchical Self-organizing Networks
Improving the correlation hunting in a large quantity of SOM component planes
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Classification of similar productivity zones in the sugar cane culture using ...
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Mapas de Kohonen como una herramienta visual de apoyo al soporte de decisione...
Mapas de Kohonen como una herramienta visual de apoyo al soporte de decisione...
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