Reservoir Computing (RC) is a high-speed machine learning framework for temporal data processing. Especially, the Echo State Network (ESN), which is one of the RC models, has been successfully applied to many temporal tasks. However, its prediction ability depends heavily on hyperparameter values. In this work, we propose a new ESN training method inspired by Generative Adversarial Networks (GANs). Our method intends to minimize the difference between the distribution of teacher data and that of generated samples, and therefore we can generate samples that reflect the dynamics in the teacher data. We apply a feedforward neural network as a discriminator so that we don’t need to use backpropagation through time in training. We justify the effectiveness of the proposed method in time series prediction tasks.
CITATION STYLE
Akiyama, T., & Tanaka, G. (2019). Echo State Network with Adversarial Training. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11731 LNCS, pp. 82–88). Springer Verlag. https://doi.org/10.1007/978-3-030-30493-5_8
Mendeley helps you to discover research relevant for your work.