We propose a stochastic agglomerative algorithm to detect the local community of some given seed vertex/vertices in network. Instead giving deterministic binary output, our method assigns every vertex value that is probability this particular would be seed. The proposed procedure has several advantages over existing algorithms, including avoiding random tie-breaking, evaluating uncertainties, d...

Various structural discontinuities, which form a discrete fracture network, play a significant role in the failure conditions and stability of the rock masses around underground excavations. Several continuum numerical methods have been used to study the stability of underground excavations in jointed rock masses but only few of them can take into account the influence of the pre-existing natur...

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...

Artificial Neural Networks (ANNs) must be able to learn by experience from environment. This property can be considered as being closely related to the approximating capabilities of the networks. Unfortunately at present only the ability of ANNs in approximating deterministic input-output mappings has been exploited. In this article it has been shown that some classes of neural networks, named ...

As a continuation of our study, this paper extends our research results of optimality-oriented stabilization from deterministic recurrent neural networks to stochastic recurrent neural networks, and presents a new approach to achieve optimally stochastic input-to-state stabilization in probability for stochastic recurrent neural networks driven by noise of unknown covariance. This approach is d...