Application of Lagrangian relaxation to computer network control

AbstractThe problem of routing and flow control in a computer communication network is formulated as a mixed integer nonlinear programming problem. The Lagrangian relaxation method is used to decompose the problem into subproblems that are relatively easier to solve than the original problem. An algorithm is then developed which can obtain an optimal solution to the problem

Cluster validity for fuzzy criterion clustering

AbstractIn this paper, we define a validity measure for fuzzy criterion clustering which is a novel approach to fuzzy clustering that in addition to being non-distance-based, addresses the cluster validity problem. The model is then recast as a bilevel fuzzy criterion clustering problem. We propose an algorithm for this model that solves both the validity and clustering problems. Our approach is validated via some sample problems

Transformation of Mass Function and Joint Mass Function for Evidence Theory in the Continuous Domain

AbstractIt has been widely accepted that expert systems must reason from multiple sources of information that is to some degree evidential-uncertain, imprecise, and occasionally inaccurate-called evidential information. Evidence theory (Dempster/ Shafer theory) provides one of the most general frameworks for representing evidential information compared to its alternatives such as Bayesian theory or fuzzy set theory. Many expert system applications require evidence to be specified in the continuous domain-such as time, distance, or sensor measurements, However, the existing evidence theory does not provide an effective approach for dealing with evidence about continuous variables. As an extension to Strat′s pioneering work, this paper provides a new combination rule, a new method for mass function2 transformation, and a new method for rendering joint mass functions which are of great utility in evidence theory in the continuous domain