In recent years, graph neural networks (GNNs) have played an important role in representation learning and successfully achieved excellent results semi-supervised classification. However, these GNNs often neglect the global smoothing of because is incompatible with node Specifically, a cluster nodes has small number other classes nodes. To address this issue, we propose graph-retraining network...

Transient stability evaluation (TSE) is part of dynamic security assessment of power systems, which involves the evaluation of the system’s ability to remain in equilibrium under credible contingencies. Neural networks (NN) have been applied to the security assessment of power systems and have shown great potential for predicting the security of power systems. This paper proposes a generalized ...

For continuous monitoring of river water quality , this study assesses the potential of using artificial neural networks (ANNs) for modeling the event-based suspended sediments concentration (SSC) in Jiasian diversion weir in southern Taiwan. The hourly data collected include the water discharge, turbidity and SSC during the storm events. The feed forward backpropagation network (BP), generaliz...

Mini-model method (MM-method) is an instance-based learning algorithm similarly as the k-nearest neighbor method, GRNN network or RBF network but its idea is different. MM operates only on data from the local neighborhood of a query. The paper presents new version of the MM-method which is based on k-means clustering algorithm. The domain of the model is calculated using k-means algorithm. Clus...

No-reference perceptual quality assessment for JPEG images in real time is a critical requirement for some applications, such as in-service visual quality monitoring, where original information can not be available. This paper proposes a no-reference perceptual quality-assessment method based on a general regression neural network (GRNN). The three visual features of artifacts introduced in JPE...

As wafer sizes increase, the clustering phenomenon of defects increases. Clustered defects cause the conventional Poisson yield model underestimate actual wafer yield, as defects are no longer uniformly distributed over a wafer. Although some yield models, such as negative binomial or compound Poisson models, consider the effects of defect clustering on yield prediction, these models have some ...

In order to study the dynamic changes in gas concentration, to reduce gas hazards, and to protect and improve mining safety, a new method is proposed to predict gas concentration. The method is based on the opposite degree algorithm. Priori and posteriori values, opposite degree computation, opposite space, prior matrix, and posterior matrix are 6 basic concepts of opposite degree algorithm. Se...

This paper attempts to employ and investigate neural based approaches as interpolation tools for modeling of Switched Reluctance Motor (SRM) drive. Precise modeling of SRM is essential to analyse the performance of control strategies for variable speed drive application. In this work the suitability of Generalized Regression Neural Network (GRNN) and Extreme Learning Machine (ELM) in addition t...

In this paper, two types of Artificial Neural Network (ANNs), Generalized Regression Neural Network (GRNN) and Radial Basis Function (RBF) have been used for heart disease to prescribe the medicine. Diagnosing the heart disease and prescribing the medicine on the basis of symptoms is a very challenging task to improve the ability of the physicians. The training capacity and medicines provided b...