Modeling dynamic human control strategy, or human skill, in
response to real-time sensing is becoming an increasingly popular
paradigm in many research areas. These models are learned from
experimental data, and as such can be characterized despite the lack
of a good physical model. Unfortunately, learned models presently
offer few, if any, guarantees in terms of model fidelity to the source
data. As such, we propose an independent, post-training model
validation procedure based on Hidden Markov Models (HMMs). The
proposed method generates a stochastic similarity measure comparing
system trajectories for the source process and the learned
models. Using this method, we are able to verify model fidelity. We
demonstrate the proposed method in the validation of neural-network
models for real-time human driving skill.
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