Many real projects complete through the realization of one and only one path of various possible network paths. Here, these networks are called alternative stochastic networks (ASNs). It is supposed that the nodes of considered network are probabilistic with exclusive-or receiver and exclusive-or emitter. First, an analytical approach is proposed to simplify the structure of t...

This dissertation uses the theory of stochastic arithmetic as a solution for the FPGA implementation of complex control algorithms for power electronics applications. Compared with the traditional digital implementation, the stochastic approach simplifies the computation involved and saves digital resources. The implementation of stochastic arithmetic is also compatible with modern VLSI design ...

Wind power generation is variable and uncertain. In the power systems with high penetration of wind power, determination of equivalent operating reserve is the main concern of systems operator. In this paper, a model is proposed to determine operating reserves in simultaneous market clearing of energy and reserve by stochastic programming based on scenarios generated via Monte Carlo simulation ...

IdentiÞcation of nonlinear stochastic processes via differential neural networks is discussed. A new ”dead-zone” type learning law for the weight dynamics is suggested. By a stochastic Lyapunov-like analysis the stability conditions for the identiÞcation error as well as for the neural network weights are established. The adaptive trajectory tracking using the obtained neural network model is r...

We demonstrate the modelling of opportunistic networks using the process algebra stochastic HYPE. Network traffic is modelled as continuous flows, contact between nodes in the network is modelled stochastically, and instantaneous decisions are modelled as discrete events. Our model describes a network of stationary video sensors with a mobile ferry which collects data from the sensors and deliv...

Stochastic neural networks which are a type of recurrent neural networks can be basicly and simply expressed as “the neural networks which are built by introducing random variations into the network”. This randomness comes from one of these usages : applying stochastic transfer functions to network neurons or determining the network weights stochastically. This randomness property makes this ty...

System reliability is an important design measure in many systems engineering fields. In this paper, we propose a new neural network method combining stochastic competitive scheme and hysteresis quantized neuron for the reliability optimization. In the proposed algorithm, the neurons are divided into two classes: One is binary neurons with stochastic competitive scheme and the other is quantize...

Several methodologies published in the literature can be used to construct realistic pore networks for simple rocks, whereas in complex pore geometry formations, as formed in tight reservoirs, such a construction still remains a challenge. A basic understanding of pore structure and topology is essential to overcome the challenges associated with the pore scale modeling of tight porous media. A...

Clustering is an important task in network analysis, with applications in fields such as biology and the social sciences. We present a novel inference algorithm for the Stochastic Block Model (SBM), a well known network clustering model. Previous inference in this model typically utilizes Markov Chain Monte Carlo or Variational Bayes, but our method is the first to utilize Stochastic Variationa...

The major function of dynamic networks is to sense information from the environment and process the information to the downstream. Therefore how to measure the information transmission ability of a dynamic network is an important topic to evaluate network performance. However, the dynamic behavior of a dynamic network is complex and, despite knowledge of network components, interactions and noi...