local bus network is the most popular transit mode and the only available transit mode in the majority of cities of the world. increasing the utility of this mode which increases its share from urban trips is an important goal for city planners. timetable setting as the second component of bus network design problem (network route design timetable setting vehicle assignment crew assignment) hav...

predicting the maintenance and management costs and replacement age of tractors in agricultural mechanized units, is important from several points. so, doing a timely agricultural operations, more accurate measure of the amount of income  including the cost of these items , determining the useful life of old tractors , replacement age, cost of the process changes and the possibility of examinin...

This paper focuses on the Call Control for the establishment and release of isochronous calls within the DAMS network (ESPRIT project #2146), a distributed ISDN PBX over an FDDI-II backbone optical ring that supports both isochronous and asynchronous traffic generated by ISDN terminals, LAN terminals, and Bandwidth On Demand (BOD) terminals. After a general overview of the DAMS network architec...

|This literature review discusses di erent methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the di erentmethodological communities, such as Bayesian, description length, and classical statistic...

Dynamic Bayesian networks (DBNs) are a class of probabilistic graphical models that has become a standard tool for modeling various stochastic time-varying phenomena. The temporal probabilistic graphical models as 2TBN are the most used and popular models for DBNs. Because of the complexity induced by adding the temporal dimension, DBN structure learning is a very complex task. Existing algorit...

We study existence, uniqueness, and stability of a class of nonlinear differential equations in the space KH of compact operators on a complex Hilbert space. The differential equations are motivated by models which arise in neural networks and have been introduced by Xu, and Chen, Amari, and Lin.

In standard automatic speech recognition (ASR), hidden Markov models (HMMs) calculate their emission probabilities by an artificial neural network (ANN) or a Gaussian distribution conditioned only upon the hidden state variable. Recent work [12] showed the benefit of conditioning the emission distributions also upon a discrete auxiliary variable, which is observed in training and hidden in reco...