Similarity Measures for Learning in Lattice Based Biomimetic Neural Networks

Gerhard X. Ritter, Gonzalo Urcid and Luis-David Lara-Rodríguez
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Gerhard X. Ritter: Computer & Information Science and Engineering Department, University of Florida (UF), Gainesville, FL 72410, USA
Gonzalo Urcid: Optics Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla 72840, Mexico
Luis-David Lara-Rodríguez: Mechatronics Engineering Department, Politechnic University of Puebla (UPP), Cuanalá, Puebla 72640, Mexico

Mathematics, 2020, vol. 8, issue 9, 1-18

Abstract: 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.

Keywords: biomimetic neural networks; dendritic computing; lattice neural networks; lattice valuations; pattern recognition; similarity measures (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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