Forward-Propagation則における高次項の効果について

概要

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We consider the forward-propagation (FP) rule for an inverse kinematics problem. This method is based on the following steps. First, we construct an approximate inverse model. Second, the goal signal in each layer is derived by the Newton-like method. Finally, the updating of weights is realized by linear multiple regression. The regression coefficients express the adding correction of the weights. However, it isdifficult to obtain an accurate inverse kinematics model because of the structure of the network. Hence, we propose the FP rule with a high order term. The layer, which is constructed with the high order term of the input to the neural network, is inserted between the input layer and the hidden layer. We apply this rule to the inverse kinematics problem for the two-link manipulator and several desired trajectory. The simulation results show that the proposed method obtains a more accurate inverse kinematics model, compared to the FP rule without the high order term.