A model of fractal hierarchical structures that share the property of non-homogeneous weighted networks is introduced. These networks can be completely and analytically characterized in terms of the involved parameters, i.e., the size of the original graph Nk and the non-homogeneous weight scaling factors r1, r2, · · · rM. We also study the average weighted shortest path (AWSP), the average deg...

The maximum weighted matching problem is to find a maximum matching in a given graph such that the sum of weights of edges in it is maximum. In this paper, the concept of maximum random fuzzy weighted matching is proposed firstly, and then the maximum random fuzzy weighted matching problem is formulated as expected value model, chance-constrained programming and dependent-chance programming acc...

Energy-constrained wireless sensor network (WSN) has attained considerable research attention in recent years and requires robust and energy efficient routing protocols for communication in fading environments to minimise the energy consumption.To Mitigate the Fading Effect in Wireless Channel ,MultiInput Multi Output(MIMO)Scheme is Utilized for Wireless Sensor Network. This Paper proposed a Cl...

In this paper we simplify the definition of the weighted sum Boolean function which used to be inconvenient to compute and use. We show that the new function has essentially the same properties as the previous one. In particular, the bound on the average sensitivity of the weighted sum Boolean function remains unchanged after the simplification.

The weighted sum method of vector objective scalarization is known to generate points on convex Pareto front whose distribution cannot be controlled. This work presents a method of improving the distribution of Pareto points generated by weighted sum method by nonlinear weight selection. Numerical examples are presented to show the effectiveness of the method.

This paper proposes a new forecasting model based on the neural network with weighted fuzzy membership functions (NEWFM) concerning forecasting of turning points in the business cycle by the composite index. NEWFM is a new model of neural networks to improve forecasting accuracy by using self adaptive weighted fuzzy membership functions. The locations and weights of the membership functions are...