scholarly journals Application of neural network method in engineering prediction

2021 ◽  
Vol 2083 (4) ◽  
pp. 042080
Author(s):  
Lizhen Jin
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Moisture Content ◽  
Numerical Estimation ◽  
Neural Network Method ◽  
Network Algorithm ◽  
Network Method ◽  
Neural Network Algorithm ◽  
Casting Production

Abstract Data mining is widely used in engineering. Neural network method is one of the methods of data mining. It has obvious advantages in engineering prediction and numerical estimation. Based on the analysis of neural network algorithm, this paper uses neural network algorithm to predict the moisture content of used sand in casting production, and obtains an effective method to judge the moisture content by voltage.

Predictive Model Based on Genetic Algorithm-Neural Network for Fatigue Performances of Pre-Corroded Aluminum Alloys

2007 ◽  
Vol 353-358 ◽  
pp. 1029-1032 ◽  
Author(s):  
Chao Hua Fan ◽  
Yu Ting He ◽  
Heng Xi Zhang ◽  
Hong Peng Li ◽  
Feng Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Aluminum Alloy ◽  
Aluminum Alloys ◽  
Predictive Model ◽  
Bp Neural Network ◽  
Neural Network Method ◽  
Network Algorithm ◽  
Network Method ◽  
Neural Network Algorithm

In the paper, genetic algorithm is introduced in the study of network authority values of BP neural network, and a GA-NN algorithm is established. Based on this genetic algorithm-neural network method, a predictive model for fatigue performances of the pre-corroded aluminum alloys under a varied corrosion environmental spectrum was developed by means of training from the testing dada, and the fatigue performances of pre-corroded aluminum alloys can be predicted. The results indicate that genetic algorithm-neural network algorithm can be employed to predict the underlying fatigue performances of the pre-corroded aluminum alloy precisely, compared with traditional neural network.

Data Mining with BP Neural Network Algorithm Based MapReduce

2013 ◽  
Vol 380-384 ◽  
pp. 2915-2919 ◽  
Author(s):  
Jian Ming Cui ◽  
Yan Xin Ye
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Bp Neural Network ◽  
Resource Constraints ◽  
Training Time ◽  
Computing Platform ◽  
Network Training ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Cloud Computing Platform

Traditional massive data mining with BP neural network algorithm, resource constraints of the ordinary stand-alone platform and scalability bottlenecks and classification process serialization due to classification inefficient results, and also have an impact on the classification accuracy. In this paper, the Detailed description of the flow of execution of the BP neural network parallel algorithm in Hadoop's MapReduce programming model.Experimental results show that: the BP neural network under the cloud computing platform can greatly shorten the network training time, better parallel efficiency and good scalability.

scholarly journals Diagnosis of Tuberculosis By Artificial Neural Network Algorithm

SinkrOn ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 223
Author(s):  
Amrin Amrin
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Cross Validation ◽  
Confusion Matrix ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Algorithm

Sangat penting bagi dokter untuk melakukan diagnosa secara dini penyakit tuberculosis agar dapat mengurangi penularan penyakit tersebut kepada masyarakat luas.  Pada penelitian ini, penulis akan menerapkan metode klasifikasi data mining, yaitu Algoritma Jaringan Syaraf Tiruan untuk mendiagnosa penyakit tuberculosis. Berdasarkan hasil pengukuran performa dari model tersebut dengan  menggunakan  metode pengujian Cross Validation, Confusion Matrix dan Kurva ROC, diketahui bahwa algoritma jaringan syaraf tiruan memiliki tingkat akurasi sebesar 89,89% dan nilai area under the curva (AUC) sebesar 0,975. Hal ini menunjukkan bahwa model yang dihasilkan termasuk katagori klasifikasi  sangat baik karena memiliki nilai AUC antara 0.90-1.00.

scholarly journals English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Dan Wu ◽  
Yuanjun Shen
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Feature Recognition ◽  
Good Effect ◽  
Data Mining Algorithm ◽  
Main Function ◽  
Network Algorithm ◽  
Neural Network Algorithm

With the development of society and the promotion of science and technology, English, as the largest universal language in the world, is used by more and more people. In the life around us, there is information in English all the time. However, because the process of manual recognition of English letters is very labor-intensive and inefficient, the demand for computer recognition of English letters is increasing. This paper studies the influence of the parameters of BP neural network and genetic algorithm on the whole network, including the input, output, and number of hidden layer nodes. Finally, it improves and determines the settings and values of the relevant parameters. On this basis, it shows the rationality of the selected parameters through experiments. The results show that only GA-BP neural network and feature data mining algorithm can complete feature extraction and become the main function of feature classification at the same time. After enough initial data sample analysis training, the GA-BP neural network was found to have good data fault tolerance and feature recognition. The experimental results show that the genetic algorithm can find the best weights and thresholds and the weights and thresholds are given to the BP neural network. After training, the recognition of handwritten letters can be realized. Finally, the convergence of the two algorithms is compared through experiments, which shows that the overall performance of the BP neural network algorithm is improved after genetic algorithm optimization. It can be seen that the genetic algorithm has a good effect in improving the BP neural network and this method has a broad prospect in English feature recognition.

scholarly journals Pemanfaatan Data Mining dalam Penentuan Rekomendasi Mustahik (Penerima Zakat)

2020 ◽  
Vol 12 (2) ◽  
pp. 67-73
Author(s):  
Andi Abdul malik Ahmad ◽  
Zawiyah Saharuna ◽  
Muhammad Fajri Raharjo
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Learning Rate ◽  
Test Results ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Artificial Neural Network Algorithm

This study applies data mining in determining recommendations for mustahik. The application is carried out using a classification method with an artificial neural network algorithm where the attributes used are age and type of work of the head of the family, the condition and ownership of the residence, the place of sewage, family monthly income, number of dependents, and diet. Tests are carried out using a combination of values ​​between learning rate, epoch, k-fold, and hidden layer neurons. Based on the test results from the classification process, it is found that the artificial neural network algorithm has the highest accuracy when the number of hidden layer neurons is six, the learning rate is one, the fold is seven, and the number of epochs is 200, which is 92.09%. The test results are then displayed on the Mustahik information system page.

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