Akademik Veri Yönetim Sistemi
Sıam Conference On The Life Sciences, Massachusetts, Amerika Birleşik Devletleri, 11 - 15 Temmuz 2016
A staggering diversity of biological and engineered systems
exhibit rhythmic behaviors, and their dynamics have been
analyzed for hundreds of years. Yet, data-driven modeling
and analysis of rhythmic behaviors remains in its infancy
and such tools are essential for understanding systems for
which “first-principles” models are not feasible. Identifying
the dynamics of rhythmic systems from input–output data
is critical to many applications in robotics and biology, and
yet remains a challenge. Here, we describe a new formulation for identifying rhythmic dynamical systems by using
harmonic transfer functions. This formulation side-steps
the well-known problem of estimating the limit cycle itself,
enables separate identification of input and measurement
delays, and applies to both hybrid and continuous dynamical systems. An important feature of our work is the selection of effective stimuli when large numbers of oscillatory
cycles cannot be easily obtained. We present preliminary
results on the application of these techniques to the identification of muscle dynamics in experiments involving ex
vivo lamprey muscle.