Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states th::�t evolve over continnous time_ A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transi­ tion model is a function of its parents. We address the problem of learning parameters ...

The patent cooperation network which enterprises join is a very important network platform for enterprises’ open innovation. However, very limited work has been done to empirically investigate the dynamic change process of the network in China. To address this issue, this paper analyzes dynamic change process of cooperation network of enterprises and the small-world effect of the biggest subgro...

We introduce a new variant of Markov decision processes called MDPs with action results, and a variant of dynamic Bayesian networks called bowties, for modeling the effects of stochastic actions. Bowties grew out of our work on decision-support systems for advisors in the US social welfare system. Bowties, and our elicitation process for them, are designed to elicit dynamic Bayesian network fra...

Several recent techniques for solving Markov decision processes use dynamic Bayesian networks to compactly represent tasks. The dynamic Bayesian network representation may not be given, in which case it is necessary to learn it if one wants to apply these techniques. We develop an algorithm for learning dynamic Bayesian network representations of Markov decision processes using data collected t...

Selection of neural network learning rates to obtain satisfactory performance from neural network controllers is a challenging problem. To assist in the selection of learning rates, this paper investigates robotic system sensitivity to neural network (NN) learning rate. The work reported here consists of experimental and simulation results. A neural network controller module, developed for the ...

abstract forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. this paper studies load consumption modeling in hamedan city province distribution network by applying esn neural network. weather forecasting data such as minimum day temperature, average day temp...

in this article, non radial method of dynamic dea with the parallel network structure is presented and is used for calculation of relative efficiency measures when inputs and outputs do not change equally. in this model, dmu divisions under evaluation have been put together in parallel. but its dynamic structure is assumed in series. since in real applications there are undesirable inputs and o...

malting is a complex biotechnological process that includes steeping; germination and drying of cereal grains under controlled conditions of temperature and humidity. in this research malting process parameters were predict by modular neural network with different activation function included, logsig-logsig, tanh-tanh, logsigtanh, logsig-identity and tanh-identity. steeping time (x1) and germin...

Integrating a geographic information system and multi-criteria decision making methods have been lead to provide spatial multi-criteria decision making methods. In this study, the spatial potential of flooding was determined based on analytic network process and analytic hierarchy process. At first, six factors of flooding were determined as criteria. The criteria were the slope, hill-slope asp...

This paper discusses a dynamic method of edge detection which works from a sequence of frames. Most edge detection algorithms process image information statically, regardless of whether the application is static i.e. whether the input is a singular, unique image or a dynamic sequence of frames used for tracking or optic flow extraction. As many applications are dynamic, such as robotics, autono...