scholarly journals SOMA Network Model Based on Native Visibility Graph

MENDEL ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 49-56
Author(s):  
Lukas Tomaszek ◽  
Ivan Zelinka
Keyword(s):  
Time Series ◽  
Network Model ◽  
Visibility Graph ◽  
New Model ◽  
Model Based ◽  
Network Properties ◽  
Basic Network ◽  
Self Organizing

In this article, we want to propose a new model of the network for analyzing the evolution algorithms.We focus on the graph called native visibility graph. We show how we can get a time series from the run ofthe self-organizing migrating algorithm and how we can convert these series into a network. At the end of thearticle, we focus on some basic network properties and we propose how can we use these properties for laterinvestigation. All experiments run on well-known CEC 2016 benchmarks.

An improved self-organizing incremental neural network model for short-term time-series load prediction

2021 ◽  
Vol 292 ◽  
pp. 116912
Author(s):  
Rong Wang Ng ◽  
Kasim Mumtaj Begam ◽  
Rajprasad Kumar Rajkumar ◽  
Yee Wan Wong ◽  
Lee Wai Chong
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Load Prediction ◽  
Short Term ◽  
Self Organizing

SIMULATING NETWORK STRUCTURES USING BERNOULLI'S PRINCIPLE

2012 ◽  
Vol 15 (05) ◽  
pp. 1250032 ◽  
Author(s):  
YICHEOL HAN ◽  
STEPHAN J. GOETZ ◽  
JEONGJAE LEE ◽  
SEONGSOO YOON
Keyword(s):  
Network Model ◽  
Network Structure ◽  
Flow Resistance ◽  
Network Structures ◽  
Model Based ◽  
Network Connections ◽  
Network Properties ◽  
Simulation Results

Using the fact that connections between vertices of a network often represent directed and weighted flows, we apply hydraulic principles to develop novel insights into network structure and growth. We develop a network model based on Bernoulli's principle and use it to analyze changes in network properties. Simulation results show that velocity of flow, resistance, fitness and existing connections in a system determine network connections of a vertex as well as overall network structure. We demonstrate how network structure is affected by changes in velocity and resistance, and how one vertex can monopolize connections within a network. Using Bernoulli's principle, we are able to independently reproduce key results in the network literature.

scholarly journals Wavelet Network Model Based on Multiple Criteria Decision Making for Forecasting Temperature Time Series

2015 ◽  
Vol 2015 ◽  
pp. 1-4 ◽  
Author(s):  
Jian Zhang ◽  
Xiao-hua Yang ◽  
Xiao-juan Chen
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Relative Error ◽  
Network Model ◽  
Multiple Criteria Decision Making ◽  
Temperature Time Series ◽  
Multiple Criteria ◽  
Wavelet Network ◽  
Model Based ◽  
Temperature Time

Due to nonlinear and multiscale characteristics of temperature time series, a new model called wavelet network model based on multiple criteria decision making (WNMCDM) has been proposed, which combines the advantage of wavelet analysis, multiple criteria decision making, and artificial neural network. One case for forecasting extreme monthly maximum temperature of Miyun Reservoir has been conducted to examine the performance of WNMCDM model. Compared with nearest neighbor bootstrapping regression (NNBR), the probability of relative error smaller than 10% increases from 65.79% to 84.21% (forecast periodT=1) and from 51.35% to 91.89%(T=2)by WNMCDM model. Similarly, the probability of relative error smaller than 20% increases from 84.21% to 97.37%(T=1)and from 81.08% to 91.89%(T=2)by WNMCDM model. Therefore, WNMCDM model is superior to NNBR model in forecasting temperature time series.

scholarly journals Unbiased Sampling of Users from (Online) Activity Data

Field Methods ◽  
2018 ◽  
Vol 31 (1) ◽  
pp. 23-38
Author(s):  
Zack W. Almquist ◽  
Sakshi Arya ◽  
Li Zeng ◽  
Emma Spiro
Keyword(s):  
Data Sets ◽  
Social Scientists ◽  
Activity Data ◽  
Simple Method ◽  
Online Activity ◽  
Model Based ◽  
Highly Active ◽  
Online Platforms ◽  
Network Properties ◽  
Basic Network

Online platforms offer new opportunities to study human behavior. However, while social scientists are often interested in using behavioral trace data—data created by a user over the course of their everyday life—to draw inferences about users, many online platforms only allow data to be sampled based on user activities (leading to data sets that are biased toward highly active users). Here, we introduce a simple method for reweighting activity-based sample statistics in order to provide descriptive (and potentially model-based) estimates of the user population. We illustrate these techniques by applying them to a case study of an online fitness community (Strava) and use it to explore basic network properties. Last, we explore the weights effect on model-based estimates for count data.

Sign in / Sign up

Export Citation Format

Share Document