S aling Kernel - Based Systems to Large Data

A robust least squares fuzzy regression model based on kernel function

In this paper, a new approach is presented to fit arobust fuzzy regression model based on some fuzzy quantities. Inthis approach, we first introduce a new distance between two fuzzynumbers using the kernel function, and then, based on the leastsquares method, the parameters of fuzzy regression model isestimated. The proposed approach has a suitable performance to<b...

An Interior Point Algorithm for Solving Convex Quadratic Semidefinite Optimization Problems Using a New Kernel Function

In this paper, we&nbsp;consider convex quadratic semidefinite optimization problems and&nbsp;provide a primal-dual Interior Point Method (IPM) based on a new&nbsp;kernel function with a trigonometric barrier term. Iteration&nbsp;complexity of the algorithm is analyzed using some easy to check&nbsp;and mild conditions. Although our proposed kernel function is&nbsp;neither a Self-Regular (SR) fun...

Access control in ultra-large-scale systems using a data-centric middleware

  The primary characteristic of an Ultra-Large-Scale (ULS) system is ultra-large size on any related dimension. A ULS system is generally considered as a system-of-systems with heterogeneous nodes and autonomous domains. As the size of a system-of-systems grows, and interoperability demand between sub-systems is increased, achieving more scalable and dynamic access control system becomes an im...

Online learning of positive and negative prototypes with explanations based on kernel expansion

The issue of classification is still a topic of discussion in many current articles. Most of the models presented in the articles suffer from a lack of explanation for a reason comprehensible to humans. One way to create explainability is to separate the weights of the network into positive and negative parts based on the prototype. The positive part represents the weights of the correct class ...

Optimal Stati Voltage-S aling for Real-Time Systems