An Identifier-Selection Method for Expressway Network Based on Toll Collection Accuracy of Multi-Path

2013 ◽  
Vol 321-324 ◽  
pp. 2260-2264
Author(s):  
Dong Bin Xu ◽  
Shu Zhen Shi ◽  
Hao Zhang
Keyword(s):  
Network Model ◽  
Selection Method ◽  
Basic Information ◽  
Selection Probability ◽  
Selection Scheme ◽  
Running Time ◽  
Candidate Strategy ◽  
Selection For ◽  
Toll Collection ◽  
The Stability

A method for identifier-selection in expressway network is proposed based on toll collection accuracy of multi-path. Using basic information of the expressway network, the network model is built. Toll collection accuracy of the model is computed by the following steps: selection for competitive and valid paths, calculation for selection probability, toll calculation and calculation for toll collection accuracy. Through multi-period and multi-candidate strategy, the identifier-selection scheme is adopted to improve the stability and save the running time. The results verify the rationality and practicality of the method.

scholarly journals Test optimization selection method based on NSGA-3 and improved Bayesian network model

2021 ◽  
Vol 39 (2) ◽  
pp. 414-422
Author(s):  
Lu Han ◽  
Xianjun Shi ◽  
Yuyao Zhai
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Selection Method ◽  
Learning Ability ◽  
Uncertain Information ◽  
Bayesian Network Model ◽  
Test Selection ◽  
Large Solution ◽  
Selection Scheme ◽  
Test Optimization

Most of the solutions to existing test selection problems are based on single-objective optimization algorithms and multi-signal models, which maybe lead to some problems such as rough index calculation and large solution set limitations. To solve these problems, a test optimization selection method based on NSGA-3 algorithm and Bayesian network model is proposed. Firstly, the paper describes the improved Bayesian network model, expounds the method of model establishment, and introduces the model's learning ability and processing ability on uncertain information. According to the constraints and objective functions established by the design requirements, NSGA-3 is used to calculate the test optimization selection scheme based on the improved Bayesian network model. Taking a certain component of the missile airborne radar as an example, the fault detection rate and isolation rate are selected as constraints, and the false alarm rate, misdiagnosis rate, test cost, and test quantity are the optimization goals. The method of this paper is used for test optimization selection. It has been verified that this method can effectively solve the problem of multi-objective test selection, and has guiding significance for testability design.

DEVELOPMENT OF THE BASIC CRITERIA FOR RUSSIAN COASTS TYPIFICATION

2017 ◽  
Author(s):  
Рубен Косян ◽  
Ruben Kosyan ◽  
Viacheslav Krylenko ◽  
Viacheslav Krylenko
Keyword(s):  
Coastal Zone ◽  
Coastal Area ◽  
Economic Activity ◽  
Basic Information ◽  
Dynamic Features ◽  
Static State ◽  
Evolutionary Features ◽  
Coastal Features ◽  
The Stability

There are many types of coasts classifications that indicate main coastal features. As a rule, the "static" state of the coasts is considered regardless of their evolutionary features and ways to further transformation. Since the most part of the coastal zone studies aimed at ensuring of economic activity, it is clear that the classification of coast types should indicate total information required by the users. Accordingly, the coast classification should include the criterion, characterizing as dynamic features of the coast and the conditions and opportunities of economic activity. The coast classification, of course, should be based on geomorphological coast typification. Similar typification has been developed by leading scientists from Russia and can be used with minimal modifications. The authors propose to add to basic information (geomorphological type of coast) the evaluative part for each coast sector. It will include the estimation of the coast changes probability and the complexity of the coast stabilization for economic activity. This method will allow to assess the dynamics of specific coastal sections and the processes intensity and, as a result – the stability of the coastal area.

A Novel Quantum Neural Network Model with Variable Selection for Short Term Load Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 612-617 ◽  
Author(s):  
Wei Sun ◽  
Yu Jun He ◽  
Ming Meng
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Short Term ◽  
Bp Network ◽  
Quantum Neural Network ◽  
Short Term Load Forecasting ◽  
Selection For

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

scholarly journals Using systematic sampling selection for Monte Carlo solutions of Feynman-Kac equations

2008 ◽  
Vol 40 (2) ◽  
pp. 454-472 ◽  
Author(s):  
Ivan Gentil ◽  
Bruno Rémillard
Keyword(s):  
Monte Carlo ◽  
Markov Chains ◽  
Selection Method ◽  
Systematic Sampling ◽  
Selection Methods ◽  
Convergence Properties ◽  
Main Motivation ◽  
Empirical Basis ◽  
Convergence Results ◽  
Selection For

While the convergence properties of many sampling selection methods can be proven, there is one particular sampling selection method introduced in Baker (1987), closely related to ‘systematic sampling’ in statistics, that has been exclusively treated on an empirical basis. The main motivation of the paper is to start to study formally its convergence properties, since in practice it is by far the fastest selection method available. We will show that convergence results for the systematic sampling selection method are related to properties of peculiar Markov chains.

scholarly journals Analysis of Selection Methods Used in Genetic Algorithms

2021 ◽  
Vol 4 ◽  
pp. 29-43
Author(s):  
Nataliya Gulayeva ◽  
Artem Ustilov
Keyword(s):  
Growth Rate ◽  
Genetic Algorithms ◽  
Selection Pressure ◽  
Fitness Function ◽  
Selection Method ◽  
Initial Population ◽  
Reproduction Rate ◽  
Selection Methods ◽  
Selection Scheme ◽  
Fitness Value

This paper offers a comprehensive review of selection methods used in the generational genetic algorithms.Firstly, a brief description of the following selection methods is presented: fitness proportionate selection methods including roulette-wheel selection (RWS) and its modifications, stochastic remainder selection with replacement (SRSWR), remainder stochastic independent selection (RSIS), and stochastic universal selection (SUS); ranking selection methods including linear and nonlinear rankings; tournament selection methods including deterministic and stochastic tournaments as well as tournaments with and without replacement; elitist and truncation selection methods; fitness uniform selection scheme (FUSS).Second, basic theoretical statements on selection method properties are given. Particularly, the selection noise, selection pressure, growth rate, reproduction rate, and computational complexity are considered. To illustrate selection method properties, numerous runs of genetic algorithms using the only selection method and no other genetic operator are conducted, and numerical characteristics of analyzed properties are computed. Specifically, to estimate the selection pressure, the takeover time and selection intensity are computed; to estimate the growth rate, the ratio of best individual copies in two consecutive populations is computed; to estimate the selection noise, the algorithm convergence speed is analyzed based on experiments carried out on a specific fitness function assigning the same fitness value to all individuals.Third, the effect of selection methods on the population fitness distribution is investigated. To do this, there are conducted genetic algorithm runs starting with a binomially distributed initial population. It is shown that most selection methods keep the distribution close to the original one providing an increased mean value of the distribution, while others (such as disruptive RWS, exponential ranking, truncation, and FUSS) change the distribution significantly. The obtained results are illustrated with the help of tables and histograms.

Selection for soyabeans with high and environmentally stable lutein concentrations

2014 ◽  
Vol 12 (S1) ◽  
pp. S12-S16 ◽  
Author(s):  
Krishna Hari Dhakal ◽  
Myoung-Gun Choung ◽  
Young-Sun Hwang ◽  
Felix B. Fritschi ◽  
J. Grover Shannon ◽  
...  
Keyword(s):  
Human Health ◽  
High Stability ◽  
Planting Dates ◽  
Early Maturity ◽  
Breeding Objective ◽  
Late Maturity ◽  
Selection For ◽  
High Concentrations ◽  
The Stability ◽  
Nutritional Benefits

Lutein has significant nutritional benefits for human health. Therefore, enhancing soybean lutein concentrations is an important breeding objective. However, selection for soybeans with high and environmentally stable lutein concentrations has been limited. The objectives of this study were to select soybeans with high seed lutein concentrations and to determine the stability of lutein concentrations across environments. A total of 314 genotypes were screened and 18 genotypes with high lutein concentrations and five genotypes with low lutein concentrations were selected for further examination. These 23 genotypes and two check varieties were evaluated under six environments (two planting dates for 2 years at one location and two planting dates for 1 year at another location). Lutein concentrations were influenced by genotype, environment and genotype × environment interactions. Genotypes with late maturity and low lutein concentrations were more stable than those with early maturity and high concentrations. Early (May) planting resulted in greater lutein concentrations than late (June) planting. Among the genotypes evaluated, PI603423B (7.7 μg/g) and PI89772 (5.8 μg/g) had the greatest mean lutein concentrations and exhibited medium and high stability across the six environments, respectively. Thus, these genotypes may be useful for breeding soybeans with high and stable seed lutein concentrations.

STABILITY AND BIFURCATION ANALYSIS ON A DELAYED NEURAL NETWORK MODEL

2005 ◽  
Vol 15 (09) ◽  
pp. 2883-2893 ◽  
Author(s):  
XIULING LI ◽  
JUNJIE WEI
Keyword(s):  
Neural Network ◽  
Hopf Bifurcation ◽  
Network Model ◽  
Neural Network Model ◽  
Center Manifold ◽  
Normal Form Theory ◽  
Center Manifold Theorem ◽  
Form Theory ◽  
Delayed Neural Network ◽  
The Stability

A simple delayed neural network model with four neurons is considered. Linear stability of the model is investigated by analyzing the associated characteristic equation. It is found that Hopf bifurcation occurs when the sum of four delays varies and passes a sequence of critical values. The stability and direction of the Hopf bifurcation are determined by applying the normal form theory and the center manifold theorem. An example is given and numerical simulations are performed to illustrate the obtained results. Meanwhile, the bifurcation set is provided in the appropriate parameter plane.

The Stability Analysis of a Class of Electric Power Network Model

2021 ◽  
Author(s):  
Shaojie Liu ◽  
Donghua Zhao ◽  
Wenrui Fan ◽  
Tie Li ◽  
Dai Cui
Keyword(s):  
Stability Analysis ◽  
Electric Power ◽  
Network Model ◽  
Power Network ◽  
The Stability

Stability indicatrices of nonnegative matrices and some of their applications in problems of biology and epidemiology

2019 ◽  
Vol 34 (5) ◽  
pp. 269-275
Author(s):  
Valery N. Razzhevaikin
Keyword(s):  
Nonnegative Matrix ◽  
Host Population ◽  
Nonnegative Matrices ◽  
Biological Communities ◽  
Evolutionary Selection ◽  
Evolutionary Optimality ◽  
Selection For ◽  
The Stability ◽  
Series Of Functions

Abstract The method of constructing a stability indicatrix of a nonnegative matrix having the form of a polynomial of its coefficients is presented. The algorithm of construction and conditions of its applicability are specified. The applicability of the algorithm is illustrated on examples of constructing the stability indicatrix for a series of functions widely used in simulation of the dynamics of discrete biological communities, for solving evolutionary optimality problems arising in biological problems of evolutionary selection, for identification of the conditions of the pandemic in a distributed host population.

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