A study of software reliability models choice based on bidirectional learning fuzzy neural network

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.

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Selecting software reliability models with a neural network meta classifier

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) ◽

10.1109/ijcnn.2008.4634336 ◽

2008 ◽

Cited By ~ 6

Author(s):

Rafael Caiuta ◽

Aurora Pozo ◽

Leonardo Emmendorfer ◽

Silvia Regina Vergilio

Keyword(s):

Neural Network ◽

Software Reliability ◽

Reliability Models ◽

Software Reliability Models

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A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

Matériaux & Techniques ◽

10.1051/mattech/2019002 ◽

2018 ◽

Vol 106 (6) ◽

pp. 603 ◽

Cited By ~ 2

Author(s):

Bendaoud Mebarek ◽

Mourad Keddam

Keyword(s):

Neural Network ◽

Experimental Data ◽

Fuzzy Neural Network ◽

Treatment Time ◽

Calculation Model ◽

Average Error ◽

Network Approach ◽

Neural Network Approach ◽

Fuzzy Neural ◽

Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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