Master of Science, University of Toledo, 2021, Engineering (Computer Science)

This research examines Echo State Network, a reservoir computer, performance using four different benchmark problems, then proposes heuristics or rules of thumb for configuring the architecture, as well as the selection of parameters and their values, which are applicable to problems within the same domain, to help serve to fill the ‘experience gap' needed by those entering this field of study. The influence of various parameter selections and their value adjustments, as well as architectural changes made to an Echo State Network, a powerful recurrent neural network configured as a reservoir computer, can be difficult to understand without experience in the field, and even some hyperparameter optimization algorithms may have difficulty adjusting parameter values without proper manual selections made first; therefore, it is imperative to understand the effects of parameters and their value selection on echo state network architecture performance for a successful build. Thus, to address the requirement for an extensive background in Echo State Network architecture, as well as examine how Echo State Network performance is affected with respect to variations in architecture, design, and parameter selection and values, a series of benchmark tasks representing different problem domains, including time series prediction, pattern generation, chaotic system prediction, and time series classification, were modeled and experimented on to show the impact on the performance of Echo State Network.

Committee: Gursel Serpen (Advisor); Kevin Xu (Committee Member); Joshua Stuckner (Committee Member); Lawrence Thomas (Committee Member) Subjects: Computer Engineering; Computer Science