Piecewise linear models of processor utilization

Piecewise linear models of chemical reaction networks

We show that certain non-linear dynamical systems with non-linearities in the form of Hill functions, can be approximated by piecewise linear dynamical systems. The resulting piecewise systems have closed form solutions that can be used to understand the behavior of the fully nonlinear system. We justify the reduction using geometric singular perturbation theory, and illustrate the results in n...

Networks of piecewise linear neural mass models

Neural mass models are ubiquitous in large scale brain modelling. At the node level they are written in terms of a set of ODEs with a nonlinearity that is typically a sigmoidal shape. Using structural data from brain atlases they may be connected into a network to investigate the emergence of functional dynamic states, such as synchrony. With the simple restriction of the classic sigmoidal nonl...

Fully-Automatic Bayesian Piecewise Sparse Linear Models

Piecewise linear models (PLMs) have been widely used in many enterprise machine learning problems, which assign linear experts to individual partitions on feature spaces and express whole models as patches of local experts. This paper addresses simultaneous model selection issues of PLMs; partition structure determination and feature selection of individual experts. Our contributions are mainly...

Processor Utilization

Piecewise Linear–Linear Latent Growth Mixture Models With Unknown Knots

Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear–linear latent growth mixture model (LGMM) for describing segmented change of individual behavior over time where the data co...