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Article

Similarity Measures for Learning in Lattice Based Biomimetic Neural Networks

1
Computer & Information Science and Engineering Department, University of Florida (UF), Gainesville, FL 72410, USA
2
Optics Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla 72840, Mexico
3
Mechatronics Engineering Department, Politechnic University of Puebla (UPP), Cuanalá, Puebla 72640, Mexico
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(9), 1439; https://doi.org/10.3390/math8091439
Received: 23 July 2020 / Revised: 12 August 2020 / Accepted: 19 August 2020 / Published: 27 August 2020
This paper presents a novel lattice based biomimetic neural network trained by means of a similarity measure derived from a lattice positive valuation. For a wide class of pattern recognition problems, the proposed artificial neural network, implemented as a dendritic hetero-associative memory delivers high percentages of successful classification. The memory is a feedforward dendritic network whose arithmetical operations are based on lattice algebra and can be applied to real multivalued inputs. In this approach, the realization of recognition tasks, shows the inherent capability of prototype-class pattern associations in a fast and straightforward manner without need of any iterative scheme subject to issues about convergence. Using an artificially designed data set we show how the proposed trained neural net classifies a test input pattern. Application to a few typical real-world data sets illustrate the overall network classification performance using different training and testing sample subsets generated randomly. View Full-Text
Keywords: biomimetic neural networks; dendritic computing; lattice neural networks; lattice valuations; pattern recognition; similarity measures biomimetic neural networks; dendritic computing; lattice neural networks; lattice valuations; pattern recognition; similarity measures
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MDPI and ACS Style

Ritter, G.X.; Urcid, G.; Lara-Rodríguez, L.-D. Similarity Measures for Learning in Lattice Based Biomimetic Neural Networks. Mathematics 2020, 8, 1439. https://doi.org/10.3390/math8091439

AMA Style

Ritter GX, Urcid G, Lara-Rodríguez L-D. Similarity Measures for Learning in Lattice Based Biomimetic Neural Networks. Mathematics. 2020; 8(9):1439. https://doi.org/10.3390/math8091439

Chicago/Turabian Style

Ritter, Gerhard X., Gonzalo Urcid, and Luis-David Lara-Rodríguez. 2020. "Similarity Measures for Learning in Lattice Based Biomimetic Neural Networks" Mathematics 8, no. 9: 1439. https://doi.org/10.3390/math8091439

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