EEG SIGNAL COMPRESSION USING RADIAL BASIS NEURAL NETWORKS

This paper describes a two-stage lossless compression scheme for electroencephalographic (EEG) signals using radial basis neural network predictors. Two variants of the radial basis network, namely, the radial basis function network and the generalized regression neural network are used in the first stage and their performances are evaluated in terms of compression ratio. The training is imparted to the network by using two training schemes, namely, single block scheme and block adaptive scheme. The compression ratios achieved by these networks when used along with arithmetic encoders in a two-stage compression scheme are obtained for different EEG test files. It is found that the generalized regression neural network performs better than other neural network models such as multilayer perceptrons and Elman network and linear predictor such as FIR.

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Daily Prediction of PM10 using Radial Basis Function and Generalized Regression Neural Network

2018 Recent Advances on Engineering, Technology and Computational Sciences (RAETCS) ◽

10.1109/raetcs.2018.8443887 ◽

2018 ◽

Cited By ~ 3

Author(s):

Vibha Yadav ◽

Satyendra Nath

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Generalized Regression Neural Network ◽

Radial Basis ◽

Generalized Regression

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A hybrid model of generalized regression neural network and radial basis function neural network for wind power forecasting in Indian wind farms

Journal of Statistics and Management Systems ◽

10.1080/09720510.2020.1721598 ◽

2020 ◽

Vol 23 (1) ◽

pp. 49-63 ◽

Cited By ~ 2

Author(s):

Jyothi Varanasi ◽

M. M. Tripathi

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Wind Power ◽

Hybrid Model ◽

Basis Function ◽

Wind Farms ◽

Generalized Regression Neural Network ◽

Wind Power Forecasting ◽

Radial Basis ◽

Generalized Regression

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Development and Comparison of Backpropagation and Generalized Regression Neural Network Models to Predict Diurnal and Seasonal Gas and PM10 Concentrations and Emissions from Swine Buildings

Transactions of the ASABE ◽

10.13031/2013.24381 ◽

2008 ◽

Vol 51 (2) ◽

pp. 685-694 ◽

Cited By ~ 16

Author(s):

G. Sun ◽

S. J. Hoff ◽

B. C. Zelle ◽

M. A. Smith

Keyword(s):

Neural Network ◽

Network Models ◽

Generalized Regression Neural Network ◽

Neural Network Models ◽

Generalized Regression

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Modeling of heavy ion collisions using radial basis function and generalized regression neural networks

Canadian Journal of Physics ◽

10.1139/p11-094 ◽

2011 ◽

Vol 89 (10) ◽

pp. 1051-1060

Author(s):

El-Sayed A. El-Dahshan

Keyword(s):

Neural Network ◽

Neural Networks ◽

Radial Basis Function ◽

Basis Function ◽

Heavy Ion Collisions ◽

Heavy Ion ◽

Generalized Regression Neural Network ◽

Ion Collisions ◽

Radial Basis ◽

Generalized Regression

Artificial neural networks (ANNs) have been applied to heavy ion collisions. In the present work, the possibility of using ANN methods for modeling the multiplicity distributions, P(ns), of shower particles produced from p, d, 4He, 6Li, 7Li, 12C, 16O, and 24Mg interactions with light (CNO) as well as heavy (AgBr) emulsions at 4.5 A GeV/c was investigated. Two different ANN approaches, namely radial basis function neural network (RBFNN) and generalized regression neural network (GRNN), were employed to obtain a mathematical formula describing these collisions. The results from RBFNN and GRNN models showed good agreement with the experimental data. GRNN models have a better performance than the RBFNN models. This study showed that the RBFNN and GRNN models are capable of accurately predicting the P(ns) of shower particles in the training and testing phases.

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Generalized Regression Neural Network and Radial Basis Function for Heart Disease Diagnosis

International Journal of Computer Applications ◽

10.5120/1325-1799 ◽

2010 ◽

Vol 7 (13) ◽

pp. 7-13 ◽

Cited By ~ 26

Author(s):

Shaikh Abdul Hannan ◽

R. R. Manza ◽

R. J. Ramteke

Keyword(s):

Neural Network ◽

Heart Disease ◽

Radial Basis Function ◽

Basis Function ◽

Disease Diagnosis ◽

Generalized Regression Neural Network ◽

Radial Basis ◽

Generalized Regression ◽

Heart Disease Diagnosis

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Performance Evaluation of Feed-Forward Neural Network Models for Handwritten Hindi Characters with Different Feature Extraction Methods

International Journal of Artificial Life Research ◽

10.4018/ijalr.2017070103 ◽

2017 ◽

Vol 7 (2) ◽

pp. 38-57

Author(s):

Gunjan Singh ◽

Sandeep Kumar ◽

Manu Pratap Singh

Keyword(s):

Neural Network ◽

Neural Networks ◽

Feature Extraction ◽

Recognition Accuracy ◽

Radial Basis Function Network ◽

Recognition Rate ◽

Extraction Methods ◽

Neural Network Models ◽

Feed Forward ◽

Radial Basis

Automatic handwritten character recognition is one of the most critical and interesting research areas in domain of pattern recognition. The problem becomes more challenging if domain is handwritten Hindi character as Hindi characters are cursive in nature and demonstrate a lot of similar features. A number of feature extraction, classification and recognition techniques have been devised and being used in this area; still the efficiency and accuracy is awaited. In this article, performance of various feed-forward neural networks is evaluated for the generalized classification of handwritten Hindi characters using various feature extraction methods. To study and analyze the performance of the selected neural networks, training and test character patterns are presented to each model and their recognition accuracy is measured. It has been analyzed that the Radial basis function network and Exact Radial basis network give highest recognition accuracy while Elman backpropagation neural network gives lowest recognition rate for most of the selected feature extraction methods.

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