A Modified Recurrent Neural Network with Parametric Bias and its Application to Action Learning of a Humanoid Robot
Takashi Kuremoto, Keita Morisaki, Kunikazu Kobayashi, Shingo Mabu, Masanao Obayashi · 2014
Tani et al.'s recurrent neural network with parametric bias (RNNPB) is able to learning different time series patterns, and it has been successfully applied in action learning of robots. In this paper, we propose a novel type RNNPB using Elmann type model instead of Jordan type in the conventional model. The proposed structure makes it easy to use error back-propagation (BP) learning algorithm which has lower computational cost than back-propagation time through (BPTT) method used in Tani et al.'s model. The effectiveness of the modified RNNPB was confirmed by its application to gesture learning experiment using a humanoid robot.