In this paper I describe an evolutionary wavelet network to optimize the filtering of a statistical time series into separate contributions. The wavelet base is regarded as a neural network where the network nodes are discrete wavelet transforms, the wavelon, and the network structure and parameters are selected through evolutionary techniques. With this combined approach I can separate stochas...

The competitive vehicle routing problem is one of the important issues in transportation area. In this paper a new method for competitive VRP with time windows and stochastic demand is introduced. In the presented method a three time bounds are given and the probability of arrival time between each time bound is assumed to be uniform. The demands of each customer are different in each time wind...

Carrier phase measurements are used for all high accuracy static GPS positioning applications. It is well known that there are two important aspects to the optimal processing of GPS measurements: the definition of the functional model, and the definition of the associated stochastic model. In most cases the functional model is sufficiently well known, while the definition of the stochastic mode...

in the present article, we focus on the numerical approximation of stochastic partial differential equations of itˆo type with space-time white noise process, in particular, parabolic equations. for each case of additive andmultiplicative noise, the numerical solution of stochastic diffusion equations is approximated using two stochastic finite difference schemes and the stability and consisten...

In the stochastic online scheduling environment, jobs with unknown release times and weights arrive over time. Upon arrival, the information on the weight of the job is revealed but the processing requirement remains unknown until the job is finished. With the objective of minimizing the total weighted completion time, various asymptotically optimal algorithms have been proposed for the online ...

Stochastic filtering is the problem of estimating the state of a dynamic system after time passes and given partial observations. It is fundamental to automatic tracking, planning, and control of real-world stochastic systems such as robots, programs, and autonomous agents. This paper presents a novel sampling-based filtering algorithm. Its expected error is smaller than sequential Monte Carlo ...