Intelligent monitoring and diagnosis of manufacturing process using an integrated approach of neural network ensemble and genetic algorithm

2008 ◽  
Vol 33 (2/3) ◽  
pp. 109 ◽  
Author(s):  
Jianbo Yu ◽  
Lifeng Xi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Manufacturing Process ◽  
Integrated Approach ◽  
Neural Network Ensemble ◽  
Intelligent Monitoring ◽  
Monitoring And Diagnosis

The Fault Diagnosis of Tower Crane Based on Genetic Algorithm and BP Neural Network

2011 ◽  
Vol 368-373 ◽  
pp. 3163-3166 ◽  
Author(s):  
Si Cong Yuan ◽  
Jing Qiang Shang ◽  
Xiao Yu Wang ◽  
Chao Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Architectural Engineering ◽  
Tower Crane ◽  
Monitoring And Diagnosis ◽  
Engineering Mechanics

As the most important architectural engineering mechanics in the processing of architectural construction, the progress of construction will be put off by the appearance of the fault of Tower Crane, so it is absolutely crucial to take the monitoring and diagnosis of the condition. BP Neural Network ,which is optimized by Genetic Algorithm, is constructed to have the prediction and identification of the fault of Tower Crane, and it proved that it is effectively and precisely to justify the fault of Tower Crane through using the structure of improving BP Neural Network.

scholarly journals PORTABLE NEAR INFRARED SPECTROMETER DENGAN SENSOR AS7263 UNTUK PENDUGAAN SIFAT KIMIA JERUK SIAM (CITRUS NOBILIS) SECARA NON-DESTRUKTIF

2021 ◽  
Vol 22 (2) ◽  
pp. 81-88
Author(s):  
Susanto B. Sulistyo ◽  
Arief Sudarmaji ◽  
Siswantoro Siswantoro ◽  
Agus Margiwiyatno ◽  
Masrukhi Masrukhi ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Transfer Function ◽  
Vitamin C ◽  
Near Infrared ◽  
Percentage Error ◽  
Neural Network Ensemble ◽  
Backpropagation Neural Network ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer

Evaluasi mutu buah jeruk secara umum masih dilakukan secara destruktif. Penelitian ini bertujuan untuk memprediksi kandungan kimia buah jeruk siam secara non-destruktif menggunakan Near Infrared Spectrometer portable dengan sensor AS7263 dan aplikasi Neural Network Ensemble (NNE) dengan genetic algorithm (GA) untuk optimasi. Keluaran dari enam channel NIRS portable digunakan sebagai input NNE. NNE yang dikembangkan terdiri atas empat buah Backpropagation Neural Network (BPNN) dengan dua buah lapisan tersembunyi dan kombinasi transfer function yang berbeda-beda. Keluaran dari keempat BPNN ini digabung untuk menghasilkan keluaran NNE yang baru dan dioptimasi menggunakan GA. Karakteristik kimia buah jeruk yang diestimasi adalah total padatan terlarut (TPT) dan vitamin C. Hasil penelitian menunjukkan bahwa akurasi estimasi NNE lebih tinggi dibandingkan akurasi sebuah BPNN tunggal. Estimasi kadar TPT buah jeruk siam menggunakan NNE berbasis GA tergolong sangat akurat dengan nilai Mean Absolut Percentage Error (MAPE) 8,04%. Adapun estimasi kadar vitamin C menggunakan NNE berbasis GA tergolong akurat dengan MAPE sebesar 11,02%. Namun demikian, hasil penelitian ini masih perlu dilanjutkan untuk mengetahui performansi alat yang dikembangkan untuk memprediksi mutu internal jeruk varietas lain yang berbeda karakteristik fisikokimianya.

scholarly journals Hybrid Ensemble Construction with Selected Neural Networks

2011 ◽  
Vol 15 (6) ◽  
pp. 652-661 ◽  
Author(s):  
M. A. H. Akhand ◽  
◽  
Pintu Chandra Shill ◽  
Kazuyuki Murase ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Task Performance ◽  
Benchmark Problems ◽  
Neural Network Ensemble ◽  
Improve Performance ◽  
Negative Correlation Learning ◽  
Optimal Network ◽  
Correlation Learning ◽  
The Individual

A Neural Network Ensemble (NNE) is convenient for improving classification task performance. Among the remarkable number of methods based on different techniques for constructing NNEs, Negative Correlation Learning (NCL), bagging, and boosting are the most popular. None of them, however, could show better performance for all problems. To improve performance combining the complementary strengths of the individual methods, we propose two different ways to construct hybrid ensembles combining NCL with bagging and boosting. One produces a pool of predefined numbers of networks using standard NCL and bagging (or boosting) and then uses a genetic algorithm to select an optimal network subset for an NNE from the pool. Results of experiments confirmed that our proposals show consistently better performance with concise ensembles than conventional methods when tested using a suite of 25 benchmark problems.

Optimum Design of a High Power Density Magnetic-Gel Brake Base Using Neural Network Model and Genetic Algorithm

2010 ◽  
Vol 102-104 ◽  
pp. 184-188
Author(s):  
Hong Zhan Lv ◽  
Xi Chang Liang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Power Density ◽  
Network Model ◽  
Neural Network Model ◽  
High Power ◽  
Computation Time ◽  
Integrated Approach ◽  
Design Parameters ◽  
High Power Density

An integrated approach based on genetic algorithm (GA) and an artificial neural network (ANN) is presented for structural optimization of a high power density magneto-gel brake. For this method, the GA method is employed for obtaining the optimal configuration of the brake by minimizing the dimensions of the brake. Subsequently, a two-layer BP neural network model is trained to obtain the correlation between main design parameters and performance of the brake, and then it is used to predict the performance of the magnetic gel brake with high power density. The coupled method incorporating the ANN with GA can reduce substantially the computation time during optimizing brake performance. Meanwhile, the proposed method’s validity is demonstrated by comparisons between the experiment and existing data.

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