Support vector regression-based model for phenol adsorption in rotating packed bed adsorber

This paper presents a reference function based 3D FLC design methodology using support vector regression (SVR) learning. The concept of reference function is introduced to 3D FLC for the generation of 3D membership functions (MF), which enhance the capability of the 3D FLC to cope with more kinds of MFs. The nonlinear mathematical expression of the reference function based 3D FLC is derived, and spatial fuzzy basis functions are defined. Via relating spatial fuzzy basis functions of a 3D FLC to kernel functions of an SVR, an equivalence relationship between a 3D FLC and an SVR is established. Therefore, a 3D FLC can be constructed using the learned results of an SVR. Furthermore, the universal approximation capability of the proposed 3D fuzzy system is proven in terms of the finite covering theorem. Finally, the proposed method is applied to a catalytic packed-bed reactor and simulation results have verified its effectiveness.

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Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

IEEJ Transactions on Power and Energy ◽

10.1541/ieejpes.136.898 ◽

2016 ◽

Vol 136 (12) ◽

pp. 898-907 ◽

Cited By ~ 2

Author(s):

Joao Gari da Silva Fonseca Junior ◽

Hideaki Ohtake ◽

Takashi Oozeki ◽

Kazuhiko Ogimoto

Keyword(s):

Support Vector Regression ◽

Solar Irradiance ◽

Training Data ◽

Data Selection ◽

Support Vector ◽

Training Data Selection

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Performance Comparison of Generalized Regression Network, Radial Basis Function Network and Support Vector Regression for Wind Power Forecasting

International Review on Modelling and Simulations (IREMOS) ◽

10.15866/iremos.v12i1.15781 ◽

2019 ◽

Vol 12 (1) ◽

pp. 16

Author(s):

Jyothi Varanasi ◽

Madan Mohan Tripathi

Keyword(s):

Radial Basis Function ◽

Support Vector Regression ◽

Wind Power ◽

Basis Function ◽

Radial Basis Function Network ◽

Performance Comparison ◽

Support Vector ◽

Wind Power Forecasting ◽

Radial Basis ◽

Function Network

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Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

10.2523/iptc-19758-ms ◽

2020 ◽

Author(s):

Avinash Wesley ◽

Bharat Mantha ◽

Ajay Rajeev ◽

Aimee Taylor ◽

Mohit Dholi ◽

...

Keyword(s):

Support Vector Regression ◽

Hydraulic Fracture ◽

Data Analytics ◽

Support Vector ◽

Fracture Geometry

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Probabilistic Support Vector Regression Classification Model for Credit Card Fraud Detection

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i9.840843 ◽

2018 ◽

Vol 6 (9) ◽

pp. 840-843

Author(s):

Nikita Sawhney ◽

B. Kaur ◽

H. Kaur

Keyword(s):

Support Vector Regression ◽

Credit Card ◽

Fraud Detection ◽

Classification Model ◽

Support Vector ◽

Credit Card Fraud ◽

Probabilistic Support

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A SUPPORT VECTOR REGRESSION AND MONTE CARLO SIMULATION - BASED INTERVAL TWO-STAGE PROGRAMMING FOR ENVIRONMENTAL SYSTEMS PLANNING IN BEIJING

Environmental Engineering and Management Journal ◽

10.30638/eemj.2019.032 ◽

2019 ◽

Vol 18 (2) ◽

pp. 329-348 ◽

Cited By ~ 1

Author(s):

Yanpeng Cai ◽

Chao Dai ◽

Yongping Li ◽

Wei Sun ◽

Guohe Huang

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Support Vector Regression ◽

Support Vector ◽

Two Stage ◽

Systems Planning ◽

Environmental Systems ◽

Simulation Based

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RISK ASSESSMENT AND CLASSIFICATION FOR DETENTION BASINS BASED ON PARTICLE SWARM OPTIMIZATION - SUPPORT VECTOR REGRESSION (PSO-SVR) IN HUAIHE RIVER BASIN, CHINA

Environmental Engineering and Management Journal ◽

10.30638/eemj.2013.226 ◽

2013 ◽

Vol 12 (9) ◽

pp. 1843-1848 ◽

Cited By ~ 1

Author(s):

Junfei Chen ◽

Shihao Zhao ◽

Huimin Wang ◽

Shufang Zhao

Keyword(s):

Risk Assessment ◽

Particle Swarm Optimization ◽

Support Vector Regression ◽

River Basin ◽

Particle Swarm ◽

Support Vector ◽

Huaihe River Basin ◽

Huaihe River ◽

Swarm Optimization ◽

Detention Basins

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IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG)

Jurnal Ilmiah Teknologi dan Rekayasa ◽

10.35760/tr.2020.25i1.2571 ◽

2020 ◽

Vol 25 (1) ◽

pp. 24-38

Author(s):

Eka Patriya

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Support Vector Regression ◽

Support Vector

Saham adalah instrumen pasar keuangan yang banyak dipilih oleh investor sebagai alternatif sumber keuangan, akan tetapi saham yang diperjual belikan di pasar keuangan sering mengalami fluktuasi harga (naik dan turun) yang tinggi. Para investor berpeluang tidak hanya mendapat keuntungan, tetapi juga dapat mengalami kerugian di masa mendatang. Salah satu indikator yang perlu diperhatikan oleh investor dalam berinvestasi saham adalah pergerakan Indeks Harga Saham Gabungan (IHSG). Tindakan dalam menganalisa IHSG merupakan hal yang penting dilakukan oleh investor dengan tujuan untuk menemukan suatu trend atau pola yang mungkin berulang dari pergerakan harga saham masa lalu, sehingga dapat digunakan untuk memprediksi pergerakan harga saham di masa mendatang. Salah satu metode yang dapat digunakan untuk memprediksi pergerakan harga saham secara akurat adalah machine learning. Pada penelitian ini dibuat sebuah model prediksi harga penutupan IHSG menggunakan algoritma Support Vector Regression (SVR) yang menghasilkan kemampuan prediksi dan generalisasi yang baik dengan nilai RMSE training dan testing sebesar 14.334 dan 20.281, serta MAPE training dan testing sebesar 0.211% dan 0.251%. Hasil penelitian ini diharapkan dapat membantu para investor dalam mengambil keputusan untuk menyusun strategi investasi saham.

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