Design of Reinforced Fuzzy Radial Basis Function Neural Networks Classifier Driven with the Aid of Iterative Learning Techniques and Support Vector-based Clustering

2020 ◽  
pp. 1-1
Author(s):  
Cheng Yang ◽  
Sung-Kwun Oh ◽  
Witold Pedrycz ◽  
Zunwei Fu ◽  
Bo Yang
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Iterative Learning ◽  
Support Vector ◽  
Learning Techniques ◽  
Radial Basis

An Empirical Analysis of Evolved Radial Basis Function Networks and Support Vector Machines with Mixture of Kernels

2015 ◽  
Vol 24 (04) ◽  
pp. 1550013 ◽  
Author(s):  
Ch. Sanjeev Kumar Dash ◽  
Pulak Sahoo ◽  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Radial Basis Function ◽  
Basis Function ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Imbalanced Dataset ◽  
Radial Basis

Classification is one of the most fundamental and formidable tasks in many domains including biomedical. In biomedical domain, the distributions of data in most of the datasets into predefined number of classes is significantly different (i.e., the classes are distributed unevenly). Many mathematical, statistical, and machine learning approaches have been developed for classification of biomedical datasets with a varying degree of success. This paper attempts to analyze the empirical performance of two forefront machine learning algorithms particularly designed for classification problem by adding some novelty to address the problem of imbalanced dataset. The evolved radial basis function network with novel kernel and support vector machine with mixture of kernels are suitably designed for the purpose of classification of imbalanced dataset. The experimental outcome shows that both algorithms are promising compared to simple radial basis function neural networks and support vector machine, respectively. However, on an average, support vector machine with mixture kernels is better than evolved radial basis function neural networks.

scholarly journals Classifying electrical activity of the brain during imaginary movements of untrained subjects using artificial neural networks

2020 ◽  
pp. 77-84
Author(s):  
Semen Kurkin ◽  
Elena Pitsik ◽  
Alexandr Khramov
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Electrical Activity ◽  
Radial Basis Function ◽  
Motor Imagery ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Support Vector ◽  
Radial Basis ◽  
Artificial Neural

Introduction: Developing new classification methods for human brain electrical activity patterns corresponding to actual movements or motor imagery is an essential interdisciplinary problem in brain-computer interface research. One of the most promising approaches is the development of methods based on artificial neural networks. Purpose: The development of ANN-based methods for classifying electroencephalographic patterns associated with motor imagery in untrained subjects. Methods: Classifiers based on linear neural networks, multi-layer perceptrons, radial basis function networks and support vector machines. Results: The authors selected the optimal type, topology, learning algorithms and parameters of an artificial neural network in order to provide the most accurate and fast classification of lower limb motor imagery EEG signals. It has been studied how the number of the analyzed channels of a multichannel EEG and their choice affect the quality of motor imagery patterns classification. Optimal configurations were obtained for the electrode arrangements. The influence of EEG pre-processing on the accuracy of motor imagery recognition was analyzed. A computational experiment showed the accuracy of 90-95% in untrained subjects. Radial basis function network demonstrated the best performance. Besides, the dataset dimensionality has been significantly reduced down to 6–12 channels without any classification accuracy loss. Practical relevance: The obtained results can be useful for the developers of motor imagery EEG classification algorithms used in brain-computer interfaces.

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