Nonparametric dynamically weighted combination model to determine when to stop testing

2020 ◽  
Vol 76 (8) ◽  
pp. 6065-6082
Author(s):  
C. A. S. Deiva Preetha ◽  
Subburaj Ramasamy
Keyword(s):  
Combination Model ◽  
Weighted Combination

scholarly journals Research on Population Development Trend in Huizhou of China Forecast Based on Optimal Weighted Combination Method and Fractional Grey Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Dewang Li ◽  
Jianbao Chen ◽  
Meilan Qiu
Keyword(s):  
Policy Implementation ◽  
Development Trend ◽  
Grey Model ◽  
Combination Method ◽  
Growth Trend ◽  
Dynamic Background ◽  
Combination Model ◽  
Predicted Values ◽  
Weighted Combination ◽  
Better Than

In this paper, the optimal weighted combination model and fractional grey model are constructed. The coefficients of the optimal weighted combination model are determined by minimizing the sum of squares of resists of each model. On the other hand, the optimal conformable fractional order and dynamic background value coefficient are determined by the quantum inspired evolutionary algorithm (QIEA). Taking the resident population from 2008 to 2018 as the research object, the optimal weighted combination model and fractional grey model were used to study the estimated and predicted values. The results are compared and analyzed. The results show that the fractional grey model is better than the optimal weighted combination model in the estimation of the values. The optimal weighted combination model is better than the fractional grey model in predicting. Meanwhile, the fractional grey model is found to be very suitable for the data values that are large, and the changes between the data are relatively small. The research results expand the application of the fractional grey model and have important implications for the policy implementation activities of Huizhou government according to the population growth trend in Huizhou.

An Efficient Particle Swarm Optimization-Based Neural Network Approach for Software Reliability Assessment

2017 ◽  
Vol 24 (04) ◽  
pp. 1750019 ◽  
Author(s):  
Pratik Roy ◽  
G. S. Mahapatra ◽  
K. N. Dey
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Software Reliability ◽  
Pso Algorithm ◽  
The Other ◽  
Swarm Optimization ◽  
Reliability Models ◽  
Combination Model ◽  
Software Reliability Models ◽  
Weighted Combination

In this paper, an artificial neural network (ANN)-based dynamic weighted combination model trained by novel particle swarm optimization (PSO) algorithm is proposed for software reliability prediction. Different software reliability growth models (SRGMs) are merged based on the weights derived by the learning algorithm of the proposed ANN. To avoid trapping in local minima during training of the ANN, we propose a neighborhood-based adaptive PSO (NAPSO) algorithm for learning of the proposed ANN in order to find global optimal weights. We conduct the experiments on real software failure data sets for validation of the proposed dynamic weighted combination model (PDWCM). Fitting performance and predictability of the proposed PSO-based neural network are compared with the conventional PSO-based neural network (CPSO) and existing ANN-based software reliability models. We also compare the performance of the proposed PSO algorithm with the CPSO algorithm through learning of the proposed ANN. Empirical results indicate that the proposed PSO and CPSO-based neural network present fairly accurate fitting and prediction capability than the other existing ANN-based software reliability models. Moreover, the proposed PSO-based neural network is most promising for the purpose of software fault prediction since it shows comparatively better fitting and prediction performance results than the other models.

Research on the measurement of CH4 concentration based on dual-band weighted combination model

2021 ◽  
Vol 17 (12) ◽  
pp. 763-768
Author(s):  
Honglian Li ◽  
Wenduo Li ◽  
Xiangyu Yan ◽  
Heshuai Lü ◽  
Fan Wang ◽  
...  
Keyword(s):  
Dual Band ◽  
Combination Model ◽  
Ch4 Concentration ◽  
Weighted Combination

Weighted Combination of Prior and Sample Information and Parameter Estimation

Optimization ◽  
1976 ◽  
Vol 7 (5) ◽  
pp. 665-672
Author(s):  
H. Burke ◽  
C. Hennig ◽  
W H. Schmidt
Keyword(s):  
Parameter Estimation ◽  
Sample Information ◽  
Weighted Combination

A combination model for operational risk estimation in a Chinese banking industry case

2012 ◽  
Vol 7 (2) ◽  
pp. 17-39 ◽  
Author(s):  
Jichuang Feng ◽  
Jianping Li ◽  
Lijun Gao ◽  
Zhongsheng Hua
Keyword(s):  
Banking Industry ◽  
Risk Estimation ◽  
Operational Risk ◽  
Combination Model ◽  
Chinese Banking Industry ◽  
Chinese Banking
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