Studies on Reservoir Initialization and Dynamics Shaping in Echo State Networks February 19
In a paper that was recently accepted at the European Symposium on Artificial Neural Networks (ESANN 2009), we look at different ways to influence the performance of echo state networks. Traditionally, echo state networks and other reservoir computing approaches use a fixed random connected reservoir, which leads to significant variation in performance. Only few problem specific optimisation procedures are known to date. We study a general initialization method using permutation matrices and derive a new unsupervised learning rule based on intrinsic plasticity (IP) for echo state networks. Using three different benchmarks, we show that networks with permutation matrices for the reservoir connectivity have much longer memory than the other methods, but are also able to perform highly non-linear mappings. We also show that IP based on sigmoid transfer functions is limited concerning the output distributions that can be achieved.
Studies on Reservoir Initialization and Dynamics Shaping in Echo State Networks,
J. Boedecker, O. Obst, N.M. Mayer, M. Asada. The full paper will be available after the conference (April) is now available.
