Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The Induction of Dynamical Recognizers
Machine Learning - Connectionist approaches to language learning
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Neural Networks - Special issue on organisation of computation in brain-like systems
Neural Computation
Adaptive mixtures of local experts
Neural Computation
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Codevelopmental Learning Between Human and Humanoid Robot Using a Dynamic Neural-Network Model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning long-term dependencies with gradient descent is difficult
IEEE Transactions on Neural Networks
Imitating others by composition of primitive actions: A neuro-dynamic model
Robotics and Autonomous Systems
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This paper proposes a novel learning method for a mixture of recurrent neural network (RNN) experts model, which can acquire the ability to generate desired sequences by dynamically switching between experts. Our method is based on maximum likelihood estimation, using a gradient descent algorithm. This approach is similar to that used in conventional methods; however, we modify the likelihood function by adding a mechanism to alter the variance for each expert. The proposed method is demonstrated to successfully learn Markov chain switching among a set of 9 Lissajous curves, for which the conventional method fails. The learning performance, analyzed in terms of the generalization capability, of the proposed method is also shown to be superior to that of the conventional method. With the addition of a gating network, the proposed method is successfully applied to the learning of sensory-motor flows for a small humanoid robot as a realistic problem of time series prediction and generation.