Inverse kinematics analysis plays an important role in developing a robot manipulator. But it is not too easy to derive the inverse kinematic equation of a robot manipulator especially robot manipulator which has numerous degree of freedom. This paper describes an application of Artificial Neural Network for modeling the inverse kinematics equation of a robot manipulator. In this case, the robot has three degree of freedoms and the robot was implemented for drilling a printed circuit board. The artificial neural network architecture used for modeling is a multilayer perceptron networks with steepest descent backpropagation training algorithm. The designed artificial neural network has 2 inputs, 2 outputs and varies in number of hidden layer. Experiments were done in variation of number of hidden layer and learning rate. Experimental results show that the best architecture of artificial neural network used for modeling inverse kinematics of is multilayer perceptron with 1 hidden layer and 38 neurons per hidden layer. This network resulted a RMSE value of 0.01474.


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    Title :

    Artificial Neural Network with Steepest Descent Backpropagation Training Algorithm for Modeling Inverse Kinematics of Manipulator


    Contributors:
    Thiang (author) / Handry Khoswanto (author) / Rendy Pangaldus (author)

    Publication date :

    2009-12-29


    Remarks:

    oai:zenodo.org:1328618
    International Journal of Electrical, Electronic and Communication Sciences 3.0(12)



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629





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