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Learning piecewise control strategies in a modular neural network architecture

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2 Author(s)
R. A. Jacobs ; Dept. of Psychol., Rochester Univ., NY, USA ; M. I. Jordan

The authors describe a multinetwork, or modular, neural network architecture that learns to perform control tasks using a piecewise control strategy. The architecture's networks compete to learn the training patterns. As a result, a plant's parameter space is adaptively partitioned into a number of regions, and a different network learns a control law in each region. This learning process is described in a probabilistic framework and learning algorithms that perform gradient ascent in a log-likelihood function are discussed. Simulations show that the modular architecture's performance is superior to that of a single network on a multipayload robot motion control task

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IEEE Transactions on Systems, Man, and Cybernetics  (Volume:23 ,  Issue: 2 )