On the approximation of the inverse dynamics of a robotic manipulator by a neural network trained with a stochastic learning algorithm
On the approximation of the inverse dynamics of a robotic manipulator by a neural network trained with a stochastic learning algorithm
Abstract
The SAGA algorithm is used to ap-proximate the inverse dynamics of a robotic manipulator with two rotational joints. SAGA (Simulated Annealing Gradient Adaptation) is a stochastic strategy for additive construction of an artificial neural network of the two-layer perceptron type based on three essential ele-ments: a) network weights update by means of the information from the gradient for the cost function; b) approval or rejection of the suggested change through a technique of clas-sical simulated annealing; and c) progressive growth of the neural network as its struc-ture reveals insufficient, using a conservative strategy for adding units to the hidden layer. Experiments are performed and efficiency is analyzed in terms of the relation between mean relative errors -in the training and test-ing sets-, network size, and computation time. The ability of the proposed technique to per-form good approximations by minimizing the complexity of the network’s architecture and, hence, the required computational memory, is emphasized. Moreover, the evolution of mini-mization processes as the cost surface is modi-fied is also discussed
Para citar este documento con norma APA sexta edición utilice:
Segura, E. (2013). On the approximation of the inverse dynamics of a robotic manipulator by a neural network trained with a stochastic learning algorithm. INGE CUC, 9(2), 39-43. Recuperado a partir de https://revistascientificas.cuc.edu.co/ingecuc/article/view/4
Segura, E. (2013). On the approximation of the inverse dynamics of a robotic manipulator by a neural network trained with a stochastic learning algorithm. INGE CUC, 9(2), 39-43. Recuperado a partir de https://revistascientificas.cuc.edu.co/ingecuc/article/view/4
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