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However, these signals are quantized in their amplitude and are discrete in time. Therefore, the time required to identify the parameters of a robot with a limited measurement system is related to an optimized motion trajectory performed by the robot. The proposed methodology consists of an algorithm that uses a trained CNN with the data created by the dynamical model of the case study robot. A processing technique is proposed to transform the position, velocity, acceleration, and torque robot signals into an image whose characteristics are extracted by the CNN to determine their dynamic parameters. The proposed algorithm does not require any optimal trajectory to find the dynamic parameters. A proposed time-spectral evaluation metric is used to validate the robot data and the identification data. The validation results show that the proposed methodology identifies the parameters of a Cartesian robot in less than 1 second, exceeding 90% of the proposed evaluation metric and 98% for the simulation results.<\/jats:p>","DOI":"10.3233\/jifs-219246","type":"journal-article","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T15:32:05Z","timestamp":1642519925000},"page":"4573-4586","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["A novel methodology of parametric identification for robots based on a CNN"],"prefix":"10.1177","volume":"42","author":[{"given":"Carlos Leopoldo","family":"Carre\u00f3n-D\u00edaz de Le\u00f3n","sequence":"first","affiliation":[{"name":"Facultad de Ciencias de la Computaci\u00f3n, Benem\u00e9rita Universidad Aut\u00f3noma de Puebla, Puebla, M\u00e9xico"}]},{"given":"Sergio","family":"Vergara-Limon","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias de 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