Stable Output Feedback in Reservoir Computing Using Ridge Regression
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
A new method for computing Moore-Penrose inverse matrices
Journal of Computational and Applied Mathematics
Self-organizing multiple models for imitation: teaching a robot to dance the YMCA
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Survey: Reservoir computing approaches to recurrent neural network training
Computer Science Review
Learning to imitate YMCA with an ESN
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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Echo state networks are a relatively new type of recurrent neural networks that have shown great potentials for solving nonlinear, temporal problems. The basic idea is to transform the low dimensional temporal input into a higher dimensional state, and then train the output connection weights to make the system output the target information. Because only the output weights are altered, training is typically quick and computationally efficient compared to training of other recurrent neural networks. This paper investigates using an echo state network to learn the inverse kinematics model of a robot simulator with feedback-errorlearning. In this scheme teacher forcing is not perfect, and joint constraints on the simulator makes the feedback error inaccurate. A novel trainingmethod which is less influenced by the noise in the training data is proposed and compared to the traditional ESN training method.