BP Neural Network Model Selection of Software Reliability Based on MATLAB

2015 ◽  
Vol 727-728 ◽  
pp. 991-995
Author(s):  
Shao Yun Song ◽  
Mao Luo ◽  
Hong Ming Zhou
Keyword(s):  
Neural Network ◽  
Software Reliability ◽  
Bp Neural Network ◽  
Matlab Simulation ◽  
Improved Method ◽  
Simulation Tools ◽  
The Neural Network ◽  
Bp Neural Network Model ◽  
Model Year ◽  
Selection Of

Research and analysis of the BP neural networkstructure and features. Find its shortcomingsand propose an improved method for the deficiencies, and establish the neural network softwarereliability of the new model.Through MATLAB simulation tools forexamples of simulation, confirmed the new model year with the traditional modelof high-precision, the characteristics of generalization stronger.

Sunlight Greenhouse Temperature Prediction Model Based on Bayesian Regularization BP Neural Network

2015 ◽  
Vol 740 ◽  
pp. 871-874
Author(s):  
Hui Zhao ◽  
Li Rong Shi ◽  
Hong Jun Wang
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Bp Neural Network ◽  
Principal Component ◽  
Coupling Factor ◽  
Bayesian Regularization ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Input Variables ◽  
Bp Neural Network Model

Directing against the problems of too large size of the neural network structure due to the existence of a complex relationship between the input coupling factor and too many input factors in establishing model for predicting temperature of sunlight greenhouse. This article chose the environmental factors that affect the sunlight greenhouse temperature as data sample. Through the principal component analysis of data samples, three main factors were extracted. These selected principal component values were taken as the input variables of BP neural network model. Use the Bayesian regularization algorithm to improve the BP neural network. The empirical results show that this method is utilized modify BP neural network, which can simplify network structure and smooth fitting curve, has good generalization capability.

The selection of adaptive region of geomagnetic map based on PCA and GA-BP neural network

2020 ◽  
Author(s):  
Yang Chong ◽  
Dongqing Zhao ◽  
Guorui Xiao ◽  
Minzhi Xiang ◽  
Linyang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Principal Component ◽  
Background Field ◽  
Characteristic Parameters ◽  
Positioning Accuracy ◽  
Matching Performance ◽  
Bp Neural Network Model ◽  
Selection Of

<p>The selection of adaptive region of geomagnetic map is an important factor that affects the positioning accuracy of geomagnetic navigation. An automatic recognition and classification method of adaptive region of geomagnetic background field based on Principal Component Analysis (PCA) and GA-BP neural network is proposed. Firstly, PCA is used to analyze the geomagnetic characteristic parameters, and the independent characteristic parameters containing principal components are selected. Then, the GA-BP neural network model is constructed, and the correspondence between the geomagnetic characteristic parameters and matching performance is established, so as to realize the recognition and classification of adaptive region. Finally, Simulation results show that the method is feasible and efficient, and the positioning accuracy of geomagnetic navigation is improved.</p>

Research of Construction Subcontracting Enterprise Competence Based on GA-BP Neural Network

2014 ◽  
Vol 1030-1032 ◽  
pp. 2664-2667
Author(s):  
Xi Kang Yan ◽  
Jing Yu Wang
Keyword(s):  
Neural Network ◽  
Quantitative Analysis ◽  
Bp Neural Network ◽  
Simulation Analysis ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Matlab Simulation ◽  
Five Dimensions ◽  
Enterprise Competence ◽  
Bp Neural Network Model

A new evaluation index system, which includes five dimensions is put forward to evaluate the competitiveness of construction subcontracting enterprise properly. Based on GA optimized BP neural network model,construction subcontracting enterprises’ competitiveness can be quantitative analysis systematically. Use of Matlab simulation analysis,research has shown that this system can well solve the problem of construction subcontracting enterprise competitiveness evaluation.

Modelling of viscosity of fluorine-free mold fluxes using neural network

2019 ◽  
Vol 116 (2) ◽  
pp. 201
Author(s):  
Xiaoli Yuan ◽  
Lin Wang ◽  
Jianqiang Zhang ◽  
Oleg Ostrovski ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Viscosity Data ◽  
The Neural Network ◽  
Mold Fluxes ◽  
Experimental Values ◽  
Bp Neural Network Model

Viscosity is an important property of mold fluxes for steel continuous casting. However, direct measurement of viscosity of multi-component systems in a broad range of temperatures and compositions is an onerous work and has some limitations. This paper developed a model using the back propagation (BP) neural network to describe the viscosity of fluorine-free mold fluxes. The BP neural network model was developed and validated using 70 experimental values of viscosity of fluorine-free mold fluxes CaO-SiO2-Al2O3-B2O3-Na2O-TiO2-MgO-Li2O-MnO-ZrO2; 51 of them were used for developing the neural network model and the rest 19 viscosity data for the model validation. Calculated viscosities were in a good agreement with the experimental data. Based on the developed model, the effects of temperature and composition on the viscosity of fluorine-free fluxes were predicted and discussed.

Simulation of Temperature Distribution during Ceramic Sintering Based on BP Neural Network Technology

2010 ◽  
Vol 105-106 ◽  
pp. 823-826
Author(s):  
Li Ping Liu ◽  
Bin Lin ◽  
Shi Huan Chen ◽  
Xiao Feng Zhang
Keyword(s):  
Neural Network ◽  
Temperature Distribution ◽  
Bp Neural Network ◽  
Training Sample ◽  
Distribution Data ◽  
The Neural Network ◽  
Ceramic Sintering ◽  
Trained Neural Network ◽  
Bp Neural Network Model ◽  
Neural Network Technology

It is difficult to calculate with finite element method or actually measure the temperature distribution of the ceramic sintering at irregular sintering curve. Trained by the temperature distribution data of ceramic sintering analyzed with ANSYS under linear sintering curves including different slopes, the neural network can be used to simulate that under irregular sintering curve at certain precision, so the temperature evolution of the ceramic hot geometry centroidal point (HGCP) can be fast obtained by the result simulated with the trained neural network. This research sets up series-parallel BP neural network model with MATLAB. The ceramic sintering date analyzed with ANSYS under linear sintering curves with ten different slopes is used as training sample of the neural network which is tested by the sample under non-linear sintering curve. The result indicates that the BP neural network is feasible for simulating temperature distribution of the ceramic sintering.

scholarly journals Modelling of Deformation Resistance with Big Data and Its Application in the Prediction of Rolling Force of Thick Plate

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shun Hu Zhang ◽  
Li Zhi Che ◽  
Xin Ying Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Rolling Force ◽  
Deformation Resistance ◽  
Force Model ◽  
The Neural Network ◽  
Bp Neural Network Model

The precision of traditional deformation resistance model is limited, which leads to the inaccuracy of the existing rolling force model. In this paper, the back propagation (BP) neural network model was established according to the industrial big data to accurately predict the deformation resistance. Then, a new rolling force model was established by using the BP neural network model. During the establishment of the neural network model, the data set of deformation resistance was established, which was calculated back from the actual rolling force data. Based on the data set after normalization, the BP neural network model of deformation resistance was established through the optimization of algorithm and network structure. It is shown that both the prediction accuracy of the neural network model on the training set and the test set are high, indicating that the generalization ability of the model is strong. The neural network model of the deformation resistance is compared with the theoretical one, and the maximum error is only 3.96%. Furthermore, by comparison with the traditional rolling force model, it is found that the prediction accuracy of the rolling force model imbedding with the present neural network model is improved obviously. The maximum error of the present rolling force model is just 3.86%. The research in this paper provides a new way to improve the prediction accuracy of rolling force model.

scholarly journals Pricing 50ETF Option Based on Genetic Algorithm BP Model

2018 ◽  
Vol 227 ◽  
pp. 02010
Author(s):  
Yulin Du
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Theory And Practice ◽  
The Neural Network ◽  
Finance Theory ◽  
S Model ◽  
Bp Neural Network Model ◽  
Bp Model

Pricing financial derivatives is focus in finance theory and practice. Comparing to the traditional parameter model pricing method, the neural network method has obvious advantages in solving this problem. In this paper,we will price the option of Shanghai 50ETF based on the improved BP neural network model (GABP). The results show that the effect of neural network is better than that of B-S model, and the accuracy of GABP model is higher than that of BP neural network model and B-S model.

An Advanced Optimization Model of Emerging Energy Priority - Development Fields Based on PS-NN Algorithm

2010 ◽  
Vol 439-440 ◽  
pp. 1030-1036
Author(s):  
Zhi Bin Liu ◽  
Yanna Su ◽  
Zhi Gang Zhang
Keyword(s):  
Neural Network ◽  
Convergence Rate ◽  
Bp Neural Network ◽  
Threshold Value ◽  
Bp Algorithm ◽  
Initial Weight ◽  
Learning Network ◽  
The Neural Network ◽  
Network Weight ◽  
Bp Neural Network Model

The scope of emerging energy is broad, and the development scale and stage of each kind of energy is also irregular. In order to choose the priority development fields of emerging energy, this paper introduces the particle swarm (PS) optimization algorithm into the neural network (NN) training based on an overall situation stochastic optimization thought, establishes the PS-BP neural network model, which optimizes the initial weight value of BP neural network using PS first, then uses the neural network to complete the study of given accuracy. The simulation results indicated that the improved PS-BP algorithm to be able to solve the slow convergence rate and easy to fall into local minimum of learning network weight and the threshold value of conventional BP algorithm effectively, has the quick convergence rate and the high evaluating precision.

Knowledge Sharing Partner Selection of Cluster Enterprise Based on BP Neural Network

2013 ◽  
Vol 333-335 ◽  
pp. 1301-1305
Author(s):  
Yu Liu ◽  
Nan Wang ◽  
Fei Feng ◽  
Lu Gan
Keyword(s):  
Neural Network ◽  
Knowledge Sharing ◽  
Knowledge Economy ◽  
Bp Neural Network ◽  
Selection Index ◽  
Partner Selection ◽  
Knowledge Innovation ◽  
Bp Neural Network Model ◽  
Innovation Knowledge ◽  
Selection Of

In the era of knowledge economy, knowledge has become the most critical enterprise resources; however, because of the accelerating speed of knowledge innovation, knowledge sharing between organizations becomes the inevitable choice for enterprises. Moreover, selection of the appropriate sharing partners has an important impact on the efficiency of knowledge sharing. In this paper, the authors analyze the factors of knowledge sharing among the cluster enterprises, build a cluster enterprise knowledge-sharing partner selection index system, and use the BP neural network model to select suitable enterprise knowledge sharing partners. Finally, the authors demonstrate the feasibility of the method with an example.

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