13,632 research outputs found
Shaping of molecular weight distribution by iterative learning probability density function control strategies
A mathematical model is developed for the molecular weight distribution (MWD) of free-radical styrene polymerization in a simulated semi-batch reactor system. The generation function technique and moment method are employed to establish the MWD model in the form of Schultz-Zimmdistribution. Both static and dynamic models are described in detail. In order to achieve the closed-loop MWD shaping by output probability density function (PDF) control, the dynamic MWD model is further developed by a linear B-spline approximation. Based on the general form of the B-spline MWD model, iterative learning PDF control strategies have been investigated in order to improve the MWD control performance. Discussions on the simulation studies show the advantages and limitations of the methodology
Numerical approximation of BSDEs using local polynomial drivers and branching processes
We propose a new numerical scheme for Backward Stochastic Differential
Equations based on branching processes. We approximate an arbitrary (Lipschitz)
driver by local polynomials and then use a Picard iteration scheme. Each step
of the Picard iteration can be solved by using a representation in terms of
branching diffusion systems, thus avoiding the need for a fine time
discretization. In contrast to the previous literature on the numerical
resolution of BSDEs based on branching processes, we prove the convergence of
our numerical scheme without limitation on the time horizon. Numerical
simulations are provided to illustrate the performance of the algorithm.Comment: 28 page
On causal extrapolation of sequences with applications to forecasting
The paper suggests a method of extrapolation of notion of one-sided
semi-infinite sequences representing traces of two-sided band-limited
sequences; this features ensure uniqueness of this extrapolation and
possibility to use this for forecasting. This lead to a forecasting method for
more general sequences without this feature based on minimization of the mean
square error between the observed path and a predicable sequence. These
procedure involves calculation of this predictable path; the procedure can be
interpreted as causal smoothing. The corresponding smoothed sequences allow
unique extrapolations to future times that can be interpreted as optimal
forecasts.Comment: arXiv admin note: substantial text overlap with arXiv:1111.670
Spline approximation of a random process with singularity
Let a continuous random process defined on be -smooth,
and have an isolated
singularity point at . In addition, let be locally like a -fold
integrated -fractional Brownian motion for all non-singular points. We
consider approximation of by piecewise Hermite interpolation splines with
free knots (i.e., a sampling design, a mesh). The approximation performance
is measured by mean errors (e.g., integrated or maximal quadratic mean errors).
We construct a sequence of sampling designs with asymptotic approximation rate
for the whole interval.Comment: 16 pages, 2 figure typos and references corrected, revised classes
definition, results unchange
Shaping of molecular weight distribution using b-spline based predictive probability density function control
Issues of modelling and control of molecular weight distributions (MWDs) of polymerization products have been studied under the recently developed framework of stochastic distribution control, where the purpose is to design the required control inputs that can effectively shape the output probability density functions (PDFs) of the dynamic stochastic systems. The B-spline Neural Network has been implemented to approximate the function of MWDs provided by the mechanism model, based on which a new predictive PDF control strategy has been developed. A simulation study of MWD control of a pilot-plant styrene polymerization process has been given to demonstrate the effectiveness of the algorithms
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