Radial Basis Function and K-Nearest Neighbor Classifiers for Studying Heart Rate Signals during Meditation

A radial basis function network (RBFN) method is proposed to reconstruct daily Sea surface temperatures (SSTs) with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°E–135°E) are selected when the tropical storm Hagibis arrived in June 2014, and these SST samples are obtained from the OISST products according to the distribution of AVHRR L2p SST and in-situ SST data. Furthermore, an improved nearest neighbor cluster (INNC) algorithm is designed to search the optimal hidden knots for RBFNs from both the SST samples and the background fields. Then the reconstructed SSTs from the RBFN method are compared with the results from the optimum interpolation (OI) method. The statistical results show that the RBFN method has a better performance of reconstructing SST than the OI method in the study, and the average RMSE is 0.48°C for the RBFN method, which is quite smaller than the value of 0.69°C for the OI method. Additionally, the RBFN methods with different basis functions and clustering algorithms are tested, and we discover that the INNC algorithm with multi-quadric function is quite suitable for the RBFN method to reconstruct SSTs when the SST samples are sparsely distributed.

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Radial basis function neural network for prediction of cardiac arrhythmias based on heart rate time series

2016 IEEE First International Conference on Control, Measurement and Instrumentation (CMI) ◽

10.1109/cmi.2016.7413789 ◽

2016 ◽

Cited By ~ 6

Author(s):

J. P. Kelwade ◽

S. S. Salankar

Keyword(s):

Neural Network ◽

Heart Rate ◽

Time Series ◽

Radial Basis Function ◽

Basis Function ◽

Cardiac Arrhythmias ◽

Radial Basis

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RBFNN Design Based on Modified Nearest Neighbor Clustering Algorithm for Path Tracking Control

Sensors ◽

10.3390/s21248349 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8349

Author(s):

Dongxi Zheng ◽

Wonsuk Jung ◽

Sunghoon Kim

Keyword(s):

Neural Network ◽

Neural Networks ◽

Radial Basis Function ◽

Basis Function ◽

Tracking Control ◽

Clustering Algorithm ◽

Nearest Neighbor ◽

Path Tracking ◽

Test Accuracy ◽

Radial Basis

Radial basis function neural networks are a widely used type of artificial neural network. The number and centers of basis functions directly affect the accuracy and speed of radial basis function neural networks. Many studies use supervised learning algorithms to obtain these parameters, but this leads to more parameters that need to be determined, thereby making the system more complex. This study proposes a modified nearest neighbor-based clustering algorithm for training radial basis function neural networks. The calculation of this clustering algorithm is not large, and it can adapt to varying densities. Furthermore, it does not require researchers to set parameters based on experience. Simulation proves that the clustering algorithm can effectively cluster samples and optimize the abnormal samples. The radial basis function neural network based on modified nearest neighbor-based clustering has higher accuracy in curve fitting than the conventional radial basis function neural network. Finally, the path tracking control based on a radial basis function neural network of a magnetic microrobot is investigated, and its effectiveness is verified through simulation. The test accuracy and training accuracy of the radial basis function neural network was improved by 23.5% and 7.5%, respectively.

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A dynamic adaptive radial basis function approach for total organic carbon content prediction in organic shale

Geophysics ◽

10.1190/geo2013-0154.1 ◽

2013 ◽

Vol 78 (6) ◽

pp. D445-D459 ◽

Cited By ~ 20

Author(s):

Maojin Tan ◽

Qiong Liu ◽

Songyang Zhang

Keyword(s):

Organic Carbon ◽

Total Organic Carbon ◽

Radial Basis Function ◽

Basis Function ◽

Nearest Neighbor ◽

Rbf Neural Network ◽

Organic Carbon Content ◽

Network Training ◽

Radial Basis ◽

Organic Shale

Total organic carbon (TOC) is an important parameter for characterizing shale gas and oil reservoirs. Estimation of TOC from well logs has previously been achieved by an empirical model. The radial basis function (RBF) neural network is a new quantitative method that can generate a smooth and continuous function of several input variables to approximate the unknown forward model. We investigated the basic principles of the RBF including network structure, basis function, network training method, and its application in the TOC prediction. The nearest neighbor algorithm was selected for the network training. Then, the Gaussian width was investigated to improve the TOC prediction accuracy through leave-one-out cross-validation. Finally, field cases of organic shale were studied for the TOC prediction, and the prediction results using the RBF method were compared with those of the [Formula: see text] method. Furthermore, according to sensitive attribute ranking, the impacts of different input logs on the predicted results were also investigated through various experiments, and the best network model was finally chosen. The error analysis between the prediction results and lab-measured TOC in some examples indicated that the new approach is more accurate than a single empirical regression method and more flexible than the [Formula: see text] method.

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