PTGNG: Parameters Tuning for Growing Neural Gas Algorithm

نویسندگان

چکیده

Growing Neural Gas (GNG) algorithm is an unsupervised learning which belongs to the competitive family. Since then, GNG has been a subject vaious developments and implementations found in literatures for two main reasons: first, number of neurons (i.e., nodes) adaptive. Meaning, it periodically changed through adding new removing old accordingly order find best network captures topological structure given data, reduce overall error that representation. Second, no restrictions when compared other algorithms, as both free space neurons. In this paper, we propose implement evolutionary based approach, namely PTGNG, tune parameters dealing with data multiple dimensional space, namely, 2D, 3D, 4D. The idea basically relies on finding optimum set parameter values any problem be solved using algorithm. by its nature searches vast applicable solutions evaluates each solution individually. When implemented our approach tuning, can note captured datasets smaller better accuracy. It also showed same results appeared working three four dimensions.

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ژورنال

عنوان ژورنال: International journal of computational and experimental science and engineering

سال: 2023

ISSN: ['2149-9144']

DOI: https://doi.org/10.22399/ijcesen.1282146