In the present paper, we develop a modified pseudospectral scheme for solving an optimal control problem which is governed by a switched dynamical system. Many real-world processes such as chemical processes, automotive systems and manufacturing processes can be modeled as such systems. For this purpose, we replace the problem with an alternative optimal control problem in which the switching t...

The reduction of a continuous Markov process with multiple metastable states to a discrete rate process is investigated in the presence of slow time-dependent parameters such as periodic external forces or slowly fluctuating barrier heights. A quantitative criterion is provided under which condition a kinetic description with time-dependent frozen rates applies and nonadiabatic corrections to t...

We examine the optimization of NLC parameters at 500, 1000, and 1500 GeV c.m. energy for speci c classes of physics processes, in particular, top and stop pair production, and W-W scattering processes. Our focus is on optimizing the luminosity spectrum, while maintaining or improving machine operability. Presented at Worldwide Study on Physics and Experiments with Future Linear e+eColliders, Ap...

e-mail : [email protected] The polyspectral parameters of a harmonic process are defined by the locations and strengths of the polyspectral impulses in the higher dimensional frequency space. MUSIC and ESPRIT-like algorithms for extracting these parameters, when the signal is corrupted by coloured Gaussian noise of unknown statistics, are proposed in this paper. The MUSIC -like algorith...

This paper describes an application of the method for modelling nonlinear dynamic systems from measurement data. The method merges the linear local model blending approach in the velocity-based linearisation form with Bayesian Gaussian process modelling. The new FixedStructure Gaussian Process model has a predetermined linear model structure with varying and probabilistic parameters represented...

Nowadays advanced machining processes are widely used by manufacturing industries in order to produce high quality precise and very complex products. These advanced machining processes involve large number of input parameters which may affect the cost and quality of the products. Selection of optimum machining parameters in such advanced machining processes is very important to satisfy all the ...

Sequential decision problems can often be modeled as Markov decision processes. Classical solution approaches assume that the parameters of the model are known. However, model parameters are usually estimated and uncertain in practice. As a result, managers are often interested in how estimation errors affect the optimal solution. In this paper we illustrate how sensitivity analysis can be perf...

In the autoregressive process of first order AR(1), a homogeneous correlated time series ut is recursively constructed as ut = q ut−1 + σ t, using random Gaussian deviates t and fixed values for the correlation coefficient q and for the noise amplitude σ. To model temporally heterogeneous time series, the coefficients qt and σt can be regarded as time-dependent variables by themselves, leading ...

The most commonly used method for estimating the time domain parameters of an autoregressive process is to use the Yule-Walker equations. The Yule-Walker estimates of the parameters of an autoregressive process of order p, or AR(p), are known to often be highly biased. This can lead to inappropriate order selection and very poor forecasting. There is a Fourier transform relationship between the...