Hierarchical neural networks consist of multiple neural networks assembled in the form of an acyclic graph. The purpose of this study is to identify the hierarchical radial basis function neural networks and select important input features for each sub-RBF neural network automatically. Based on the pre-defined instruction/operator sets, a hierarchical RBF neural network can be created and evolv...

The present study introduced a novel hierarchical hub set covering problem with capacity constraints. This study showed the significance of fixed charge costs for locating facilities, assigning hub links and designing a productivity network. The proposed model employs mixed integer programming to locate facilities and establish links between nodes according to the travel time between an origin-...

In this paper, we argue a method to collect information of each existing multicast flow on hierarchical networks. SRSVP, a QoS-based multicast routing protocol, is designed as it collects flow-specific information, called PQ, by putting it into signaling messages, so that the derived QoS path becomes more efficient. HQLIP, an underlying QoS-based unicast routing protocol, handles a network as a...

Introduction The vertical and horizontal movements of the Earth crust have caused extensive changes in surface phenomena, in the active tectonic regions. In order to measure some of these changes, morphometry analyses have been used to evaluation of the amount of tectonic activity. These analyses are useful tools for analyzing existing feature at the ground level and provide a proper understan...

This paper presents the research work addressing optimal secure and protected Multicasting in wired and wireless Hierarchical Sensor Networks (HSN). The multicast nodes in a hierarchical set up are associated with” Importance values” that are normalized into probabilities. The security constraint imposed is associated with the concept of “Prefix-free paths” in the associated graph. The optimali...

BACKGROUND Gene expression as governed by the interplay of the components of regulatory networks is indeed one of the most complex fundamental processes in biological systems. Although several methods have been published to unravel the hierarchical structure of regulatory networks, weaknesses such as the incorrect or inconsistent assignment of elements to their hierarchical levels, the incapabi...

Hierarchical-ring based multiprocessors are interesting alternatives to the more popular two-dimensional direct networks. They allow for simple router designs and wider communication paths than their direct network counterparts. There are several ways hierarchical-ring networks can be configured for a given number of processors. Feasible topologies range from tall, lean networks to short, wide ...

Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and fi...

In this paper, firstly, we study analytically the topological features of a family of hierarchical lattices (HLs) from the view point of complex networks. We derive some basic properties of HLs controlled by a parameter q: scale-free degree distribution with exponent γ = 2+ ln 2 ln q , null clustering coefficient, power-law behavior of grid coefficient, exponential growth of average path length...

In this paper we present a hierarchical loss network model for estimating the end-to-end blocking probabilities for large networks. As networks grow in size, nodes tend to form clusters geographically and hierarchical routing schemes are more commonly used. Loss network and reduced load models are often used to approximate end-to-end call blocking probabilities and hence throughput. However so ...