Flexible robot system is in general taken into real consideration as most important\nprocess in a number of academic and industrial environments. Due to the fact that the\naforementioned system is so applicable in real domains, the novel ideas with respect\nto state-of-the-art in outperforming its performance are always valuable. With this\npurpose, a number of the soft computing techniques can be preferred with reference\nto the traditional ones to predict and optimize the overall performance of the abovecaptioned\nprocess. The approach proposed here is in fact organized in line with the\nintegration of the fuzzy-based approach in association with the neural networks, in\norder to enable the process under control to be capable of learning and adapting to\nbe matched, in a number of real environments. It can be shown that the outcomes\ntolerate the imprecise circumstances, as one of advantages regarding the fuzzy-based\napproach. In the present investigation, a new hybrid approach is proposed to deal\nwith the arm of flexible robot system through the neural networks, the fuzzy-based\napproach and also the particle swarm optimization. It should be noted that the objective\nof the proposed research is to control the claw of robot system including twodegree-\nof-freedom movable arms. The results indicate that the mean-square error and\nthe root-mean-square error are accurately outperformed with reference to the traditional\nones, tangibly.
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