Abstract

Reservoir computers rely on an internal network to predict the future state(s) of dynamical processes. To understand how a reservoir's accuracy depends on this network, we study how varying the networ's topology and scaling affects the reservoir's ability to predict the chaotic dynamics on the Lorenz attractor. We define a metric for diversity, the property describing the variety of the responses of the nodes that make up reservoir's internal network. We use Bayesian hyperparameter optimization to find optimal hyperparameters and explore the relationships between diversity, accuracy of model predictions, and model hyperparameters. The content regarding the VPT metric, the effects of network thinning on reservoir computing, and the results from grid search experiments mentioned in this thesis has been done previously. The results regarding the diversity metric, kernel tests, and results from BHO are new and use this previous work as a comparison to the quality and usefulness of these new methods in creating accurate reservoir computers.

Degree

College and Department

Computational, Mathematical, and Physical Sciences; Mathematics

Rights

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