Abstract
This article proposes a novel advanced differential evolution method which combines the differential evolution with the modified back-propagation algorithm. This new proposed approach is applied to train an adaptive enhanced neural model for approximating the inverse model of the industrial robot arm. Experimental results demonstrate that the proposed modeling procedure using the new identification approach obtains better convergence and more precision than the traditional back-propagation method or the lonely differential evolution approach. Furthermore, the inverse model of the industrial robot arm using the adaptive enhanced neural model performs outstanding results.
Highlights
IntroductionThe differential evolution (DE) algorithm is promising for identifying and controlling nonlinear dynamic system
According to recent studies, the differential evolution (DE) algorithm is promising for identifying and controlling nonlinear dynamic system
The DE-trained neural networks for nonlinear dynamic system identification are introduced in the literatures.[3,4,5]
Summary
The differential evolution (DE) algorithm is promising for identifying and controlling nonlinear dynamic system. A new adaptive neural MIMO NARX model was proposed based on an advanced DE (advanced differential evolution (ADE)-AMNM) for inverse model of the three degree of freedom (3-DOF) industrial robot arm identification. This new advanced DE approach links between the DE with the modified back-propagation (MBP) algorithm which is applied to optimally produce the weighting values of neural model. The proposed adaptive neural MIMO NARX model using the advanced DE algorithm (ADE-AMNM) for inverse kinematics of the robot arm identification approach successfully modeled and performed well.
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