Eusebio Jiménez LópezDaniel Servín de la Mora-PulidoLuis Alfonso Reyes-ÁvilaRaúl Servín de la Mora-PulidoJavier Melendez-CamposAldo López-Martínez2026-03-222026-03-22202110.1017/s0263574720001071https://doi.org/10.1017/s0263574720001071https://andeanlibrary.org/handle/123456789/45430Citaciones: 23SUMMARY This paper presents a novel method for modeling a 3-degree of freedom open kinematic chain using quaternions algebra and neural network to solve the inverse kinematic problem. The structure of the network was composed of 3 hidden layers with 25 neurons per layer and 1 output layer. The network was trained using the Bayesian regularization backpropagation. The inverse kinematic problem was modeled as a system of six nonlinear equations and six unknowns. Finally, both models were tested using a straight path to compare the results between the Newton–Raphson method and the network training.enKinematicsInverse kinematicsArtificial neural networkBackpropagationQuaternionNonlinear systemRegularization (linguistics)Computer scienceInverseKinematics equationsarticle