RBF Neural Networks Optimization Algorithm Based on Support Vector Machine and Its Application

2010 2nd International Conference on Information Engineering and Computer Science ◽

10.1109/iciecs.2010.5678389 ◽

2010 ◽

Author(s):

Jinxia Ren ◽

Sai Yang

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Optimization Algorithm ◽

Support Vector ◽

Rbf Neural Networks ◽

Neural Networks Optimization

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Short Time Series of Website Visits Prediction by RBF Neural Networks and Support Vector Machine Regression

Artificial Intelligence and Soft Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-642-29347-4_16 ◽

2012 ◽

pp. 135-142 ◽

Author(s):

Vladimir Olej ◽

Jana Filipova

Keyword(s):

Neural Networks ◽

Time Series ◽

Support Vector Machine ◽

Support Vector ◽

Rbf Neural Networks ◽

Support Vector Machine Regression ◽

Short Time Series ◽

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A Comparative Study of Supplier Selection Based on Support Vector Machine and RBF Neural Networks

International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012) Proceedings ◽

10.1007/978-3-642-38445-5_96 ◽

2013 ◽

pp. 917-926

Author(s):

Zao-jie Kong ◽

Jun-bin Xue

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Comparative Study ◽

Supplier Selection ◽

Support Vector ◽

Rbf Neural Networks

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RBF Neural Networks Optimization Algorithm and Application on Tax Forecasting

TELKOMNIKA Indonesian Journal of Electrical Engineering ◽

10.11591/telkomnika.v11i7.2199 ◽

2013 ◽

Vol 11 (7) ◽

Author(s):

YU Zhijun

Keyword(s):

Neural Networks ◽

Optimization Algorithm ◽

Rbf Neural Networks ◽

Neural Networks Optimization

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A Hybrid Method of Least Square Support Vector Machine and Bacterial Foraging Optimization Algorithm for Medium Term Electricity Price Forecasting

International Journal of Integrated Engineering ◽

10.30880/ijie.2019.11.03.024 ◽

2019 ◽

Vol 11 (3) ◽

Author(s):

Intan Azmira Wan Abdul Razak ◽

◽

Nik Nur Atira Nik Ibrahim ◽

Izham Zainal Abidin ◽

Yap Keem Siah ◽

...

Keyword(s):

Support Vector Machine ◽

Optimization Algorithm ◽

Least Square ◽

Support Vector ◽

Price Forecasting ◽

Medium Term ◽

Bacterial Foraging Optimization ◽

Electricity Price Forecasting ◽

Method Of Least Square ◽

Bacterial Foraging Optimization Algorithm

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Estimation of rainfed maize transpiration under various mulching methods using modified Jarvis-Stewart model and hybrid support vector machine model with whale optimization algorithm

Agricultural Water Management ◽

10.1016/j.agwat.2021.106799 ◽

2021 ◽

Vol 249 ◽

pp. 106799

Author(s):

Jing Zheng ◽

Junliang Fan ◽

Fucang Zhang ◽

Lifeng Wu ◽

Yufeng Zou ◽

...

Keyword(s):

Support Vector Machine ◽

Optimization Algorithm ◽

Support Vector Machine Model ◽

Whale Optimization Algorithm ◽

Support Vector ◽

Machine Model ◽

Whale Optimization

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Prediction of the resilient modulus of non-cohesive subgrade soils and unbound subbase materials using a hybrid support vector machine method and colliding bodies optimization algorithm

Construction and Building Materials ◽

10.1016/j.conbuildmat.2020.122140 ◽

2021 ◽

Vol 275 ◽

pp. 122140

Author(s):

Nasrin Heidarabadizadeh ◽

Ali Reza Ghanizadeh ◽

Ali Behnood

Keyword(s):

Support Vector Machine ◽

Optimization Algorithm ◽

Resilient Modulus ◽

Support Vector ◽

Machine Method ◽

Support Vector Machine Method ◽

Subgrade Soils ◽

Colliding Bodies Optimization

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Shape optimization of GFRP elastic gridshells by the weighted Lagrange ε-twin support vector machine and multi-objective particle swarm optimization algorithm considering structural weight

10.1016/j.istruc.2021.05.077 ◽

2021 ◽

Vol 33 ◽

pp. 2066-2084

Author(s):

Soheila Kookalani ◽

Bin Cheng ◽

Sheng Xiang

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Shape Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Twin Support Vector Machine ◽

Support Vector ◽

Swarm Optimization ◽

Multi Objective

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A Modified Teaching and Learning Based Optimization Algorithm and Application in Deep Neural Networks Optimization for Electro-Discharge Machining

Lecture Notes in Electrical Engineering - Advanced Manufacturing and Automation VII ◽

10.1007/978-981-10-5768-7_64 ◽

2018 ◽

pp. 605-615

Author(s):

Chen Wang ◽

Baorui Li ◽

Yi Wang ◽

Kesheng Wang ◽

Shenghuai Wang

Keyword(s):

Neural Networks ◽

Optimization Algorithm ◽

Teaching And Learning ◽

Deep Neural Networks ◽

Electro Discharge Machining ◽

Neural Networks Optimization

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Research on Network Intrusion Detection Based on Support Vector Machine Optimized with Pigeon-inspired Optimization Algorithm

2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS) ◽

10.1109/idaacs-sws.2018.8525752 ◽

2018 ◽

Author(s):

Yiheng Sun ◽

Zhiwei Ye ◽

Chunzhi Wang ◽

Lingyu Yan ◽

Ruoxi Wang

Keyword(s):

Support Vector Machine ◽

Intrusion Detection ◽

Optimization Algorithm ◽

Network Intrusion Detection ◽

Support Vector ◽

Network Intrusion

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Sensor Dynamic Modeling Based on LS-SVM and NGA

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.381-382.439 ◽

2008 ◽

Vol 381-382 ◽

pp. 439-442

Author(s):

Qi Wang ◽

Zhi Gang Feng ◽

K. Shida

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Support Vector Machine ◽

Least Squares ◽

Dynamic Model ◽

Dynamic Modeling ◽

Support Vector ◽

Local Minima ◽

Highly Nonlinear ◽

Sharing Function

Least squares support vector machine (LS-SVM) combined with niche genetic algorithm (NGA) are proposed for nonlinear sensor dynamic modeling. Compared with neural networks, the LS-SVM can overcome the shortcomings of local minima and over fitting, and has higher generalization performance. The sharing function based niche genetic algorithm is used to select the LS-SVM parameters automatically. The effectiveness and reliability of this method are demonstrated in two examples. The results show that this approach can escape from the blindness of man-made choice of LS-SVM parameters. It is still effective even if the sensor dynamic model is highly nonlinear.

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