Slope Deformation Prediction Based on Chaotic-SVM

2013 ◽  
Vol 353-356 ◽  
pp. 673-677
Author(s):  
Lei Jia ◽  
Yuan Li ◽  
Yong Ping Xie
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Slope Deformation ◽  
Support Vector ◽  
Single Variable ◽  
Deformation Prediction ◽  
Slope Engineering ◽  
Training Samples ◽  
Slope Displacement ◽  
Fitting Accuracy

This paper principally studies the prediction of slope deformation based on Support Vector Machine (SVM). To explore the prediction process, phase space is reconstructed. The geological body’s displacement data obtained from chaotic time series are used as SVM’s training samples. Slope displacement caused by multivariable coupling is predicted by means of single variable. Results show that this model is of high fitting accuracy and generalization, and provides reference for deformation prediction in slope engineering.

scholarly journals Slope Deformation Prediction Based on Support Vector Machine

2013 ◽  
Vol 6 (1) ◽  
pp. 115-118
Author(s):  
Lei JIA ◽  
◽  
Yuan LI ◽  
Yongping XIE ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Slope Deformation ◽  
Support Vector ◽  
Deformation Prediction

Membership Calculation Based on Dimension Hierarchical Division

2013 ◽  
Vol 475-476 ◽  
pp. 312-317
Author(s):  
Ping Zhou ◽  
Jin Lei Wang ◽  
Xian Kai Chen ◽  
Guan Jun Zhang
Keyword(s):  
Support Vector Machine ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Bottom Up ◽  
Generalization Ability ◽  
Clustering Technique ◽  
Novel Approach ◽  
Training Samples ◽  
Noise Sample

Since dataset usually contain noises, it is very helpful to find out and remove the noise in a preprocessing step. Fuzzy membership can measure a samples weight. The weight should be smaller for noise sample but bigger for important sample. Therefore, appropriate sample memberships are vital. The article proposed a novel approach, Membership Calculate based on Hierarchical Division (MCHD), to calculate the membership of training samples. MCHD uses the conception of dimension similarity, which develop a bottom-up clustering technique to calculate the sample membership iteratively. The experiment indicates that MCHD can effectively detect noise and removes them from the dataset. Fuzzy support vector machine based on MCHD outperforms most of approaches published recently and hold the better generalization ability to handle the noise.

PERBANDINGAN METODE PREDIKSI SUPPORT VECTOR MACHINE DAN LINEAR REGRESSION MENGGUNAKAN BACKWARD ELIMINATION PADA PRODUKSI MINYAK KELAPA

2019 ◽  
Vol 4 (2) ◽  
pp. 104-107
Author(s):  
Andi Bode
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Linear Regression ◽  
Support Vector ◽  
Backward Elimination

Pohon kelapa banyak dimanfaatkan oleh manusia, sehingga tumbuhan ini dianggap tumbuhan serbaguna, salah satunya minyak kelapa yang dihasilkan oleh buah pohon kelapa. Produksi jumlah minyak kelapa menjadi bagian penting disetiap perusahaan yang bergerak di bidang produksi dengan tujuan mencapai target hasil produksi. Namaun Produksi minyak setiap hari mengalami perubahan fluktuatif. Perusahaan sangat memerlukan prediksi jumlah produksi. Penelitian ini bermaksud membandingakn metode support vector machine dan linear regression mengunakan fitur seleksi backward elimination berdasarkan data time series Sales Order. Hasil penelitian pada dataset sales order dengan menggunakan metode Support Vector Machine (SVM) didapatkan RMSE 0,127, dengan menggunakan metode SVM dan Backward Elimination (BE) didapatkan RMSE 0,115, dengan metode Linear Regression (LR) didapatkan RMSE 0,118 dan dengan menggunakan metode LR dan Backward Elimination didapatkan RMSE 0,118.  Dari hasil perbandingan tersebut dapat disimpulkan bahwa kinerja SVM menggunakan Backward Elimination lebih baik dibanding SVM, LR dan LR menggunakan Backward Elimination

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