Fault diagnosis has always been an essential aspect of control system design. This is necessary due to the growing demand for increased performance and safety of industrial systems is discussed. Support vector machine classifier is a new technique based on statistical learning theory and is designed to reduce structural bias. Support vector machine classification in many applications in v...

in this paper, a robust integral of the sign error (rise) feedback controller is designed for a rigid-link electrically driven (rled) robot manipulator actuated by direct current dc motor in presence of parametric uncertainties and additive disturbances. rise feedback with implicitly learning capability is a continuous control method based on the lyapunov stability analysis to compensate an add...

support vector machine (svm) was used to analyze the occurrence of roach in flemish stream basins (belgium). several habitat and physico?chemical variables were used as inputs for the model development. the biotic variable merely consisted of abundance data which was used for predicting presence/absence of roach. genetic algorithm (ga) was combined with svm in order to select the most important...

the lithologies of regions, which located near the collision zone, are very different from other geology setting. mapping in these areas needs extensive and exact studies and tools because of the variety of rocks, intensive tectonic uplift and complicated units. hyperspectral sensor is one of the most advanced tools with hundreds of bands that each measures a very narrow range of wavelengths an...

background: we aimed to assess the high-risk group for suicide using different classification methods includinglogistic regression (lr), decision tree (dt), artificial neural network (ann), and support vector machine (svm). methods: we used the dataset of a study conducted to predict risk factors of completed suicide in hamadan province, the west of iran, in 2010. to evaluate the high-risk grou...

support vector regression (svr) solves regression problems based on the concept of support vector machine (svm). in this paper, a new model of svr with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...

Klinkenberg permeability is an important parameter in tight gas reservoirs. There are conventional methods for determining it, but these methods depend on core permeability. Cores are few in number, but well logs are usually accessible for all wells and provide continuous information. In this regard, regression methods have been used to achieve reliable relations between log readings and Klinke...

klinkenberg permeability is an important parameter in tight gas reservoirs. there are conventional methods for determining it, but these methods depend on core permeability. cores are few in number, but well logs are usually accessible for all wells and provide continuous information. in this regard, regression methods have been used to achieve reliable relations between log readings and klinke...

seismic facies analysis (sfa) aims to classify similar seismic traces based on amplitude, phase,frequency, and other seismic attributes. sfa has proven useful in interpreting seismic data, allowingsignificant information on subsurface geological structures to be extracted. while facies analysis hasbeen widely investigated through unsupervised-classification-based studies, there are few casesass...