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  • In Jour Smart Sensing And Intelligent Systems

 

Research Article | 01-June-2017

APPLICATION OF RADIAL BASIS FUNCTION NEURAL NETWORK TO PREDICT EXCHANGE RATE WITH
FINANCIAL TIME SERIES

The literature indicates that exchange rates are largely unforecastable from the fact that the overwhelming majority of studies have employed linear models in forecasting exchange rates. In this paper, we applied Radial Basis Function Neural Network (RBFNN) to predict exchange rate, motived by the fact that RBFNN have the ability to implicitly detect complex nonlinear relationships between dependent and independent variables as it “learns” the relationship inherent in the exchange rate data

AI SUN, JUI-FANG CHANG

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 2, 308–326

Article | 01-September-2015

Classification of artificial light sources and estimation of Color Rendering Index using RGB sensors, K Nearest Neighbor and Radial Basis Function

J.-S. Botero V., F.-E. López G., J.-F. Vargas B.

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1505–1524

Research Article | 27-December-2017

ERROR MODELING AND COMPENSATION OF 3D SCANNING ROBOT SYSTEM BASED ON PSO-RBFNN

In order to improve the measurement accuracy of three-dimensional (3D) scanning robot, a method of 3D scanning robot system error modeling and compensation based on particle swarm optimization radial basis function neural network (PSO-RBFNN) is proposed to achieve intelligent compensation of measurement error. The structure, calibration and error modeling process of 3D scanning robot system are mainly described. Cleverly using the iterative closest point (ICP) algorithm to construct

Jianhong Qi, Jinda Cai

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 2, 837–855

Research paper | 10-April-2013

OBSERVER BASED DYNAMIC SURFACE CONTROL OF A HYPERSONIC FLIGHT VEHICLE

This paper describes the design and analysis of a proportional integral air speed controller and a nonlinear adaptive dynamic surface altitude controller for the longitudinal dynamics of a generic hypersonic flight vehicle. The uncertain nonlinear functions in the pure feedback flight vehicle model are approximated by using radial basis function neural networks. For the controller design, the complete states are assumed to be available for measurement, then a sliding mode observer is

W. A. Butt

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 2, 664–688

Research paper | 10-April-2013

NEURAL NETWORKS METHOD APPLIED TO THE PROPERTY STUDY OF STEEL-CONCRETE COMPOSITE
COLUMNS UNDER AXIAL COMPRESSION

coefficient(the ratio of the diameter of the core steel tube to the overall width of the column section). RBFNNs(Radial Basis Function Neural Networks) are employed for calculated the loading capacity of the concrete-filled core steel tube with outer angle steel plank reinforecd concret stub columns under axial compression, and the prediction results based on RBFNNS are compared with theoretical formula calculation results. The maximum and minimum error ratio of prediction is 12.32% and 4.17

Jianming Liu

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 2, 548–566

Research Article | 01-December-2017

PERFORMANCE EVALUATION OF SVM KERNELS ON MULTISPECTRAL LISS III DATA FOR OBJECT CLASSIFICATION

the LIBSVM package. In this paper, Support Vector Machine (SVM) is evaluated as classifier with four different kernels namely linear kernel, polynomial kernel, radial basis function kernel and sigmoid kernel. Several datasets are being experimented to find out the performance of various kernels of SVM .By changing the value of ‘C’ and γ varying results are observed. Among these RBF kernel with a value of C = 1000 and gamma=0.75 got an excellent accuracy of 99.1509%.The SVM-RBF

S.V.S. Prasad, T. Satya Savithri, Iyyanki V. Murali Krishna

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 4, 829–844

Research Article | 01-December-2017

PERFORMANCE EVALUATION OF SVM KERNELS ON MULTISPECTRAL LISS III DATA FOR OBJECT CLASSIFICATION

the LIBSVM package. In this paper, Support Vector Machine (SVM) is evaluated as classifier with four different kernels namely linear kernel, polynomial kernel, radial basis function kernel and sigmoid kernel. Several datasets are being experimented to find out the performance of various kernels of SVM .By changing the value of ‘C’ and γ varying results are observed. Among these RBF kernel with a value of C = 1000 and gamma=0.75 got an excellent accuracy of 99.1509%.The SVM-RBF kernel gave an edge

S.V.S. Prasad, T. Satya Savithri, Iyyanki V. Murali Krishna

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 4, 863–878

Article | 01-June-2016

A MACHINE VISION SYSTEM FOR ESTIMATION OF THEAFLAVINS AND THEARUBIGINS IN ORTHODOX BLACK TEA

Amitava Akuli, Abhra Pal, Gopinath Bej, Tamal Dey, Arunangshu Ghosh, Bipan Tudu, Nabarun Bhattacharyya, Rajib Bandyopadhyay

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 2, 709–731

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