Reducing Complexity of Echo State Networks with Sparse Linear Regression Algorithms

In this paper the use of sparse linear regression algorithms in echo state networks (ESN) is presented for reducing the number of readouts and improving the robustness and generalization properties of ESNs. Three data sets with overall 80 tests are used to validate the use of sparse linear regression algorithms for echo state networks. It is shown that it is possible to increase accuracy on the test data sets, not used in the ESN training phase, and in the same time reduce the overall number of the required readouts when compared to the standard approach of using ridge linear regression on the echo state network readouts.