Immunization dynamics on a two-layer network model

Immunization Dynamics on a 2-layer Network Model

We introduce a 2-layer network model for the study of the immunization dynamics in epidemics. Spreading of an epidemic is modeled as an excitatory process in a small-world network (body layer) while immunization by prevention for the disease as a dynamic process in a scale-free network (head layer). It is shown that prevention indeed turns periodic rages of an epidemic into small fluctuation. T...

Intelligent identification of vehicle’s dynamics based on local model network

This paper proposes an intelligent approach for dynamic identification of the vehicles. The proposed approach is based on the data-driven identification and uses a high-performance local model network (LMN) for estimation of the vehicle’s longitudinal velocity, lateral acceleration and yaw rate. The proposed LMN requires no pre-defined standard vehicle model and uses measurement data to identif...

Two-Population Dynamics in a Growing Network Model

We introduce a growing network evolution model with nodal attributes. The model describes the interactions between potentially violent V and nonviolent N agents who have different affinities in establishing connections within their own population versus between the populations. The model is able to generate all stable triads observed in real social systems. In the framework of rate equations th...

A novel two-layer neural network classifier

Generalization in a two-layer neural network.

Learning of a fully connected two-layer neural networks with N input nodes, M hidden nodes and a single output node is studied using the annealed approximation. We study the generalization curve, i.e. the average generalization error as a function of the number of the examples. When the number of examples is the order of N, the generalization error is rapidly decreasing and the system is in a p...