A comparative study of radial basis function neural networks in dynamic clustering algorithm

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 Semi-Online Training Algorithm for the Radial Basis Function Neural Networks

Advances in Banking Technology and Management ◽

10.4018/978-1-59904-675-4.ch015 ◽

2008 ◽

pp. 243-260 ◽

Cited By ~ 5

Author(s):

Vadlamani Ravi ◽

P. Ravi Kumar ◽

Eruku Ravi Srinivas ◽

Nikola K. Kasabov

Keyword(s):

Neural Networks ◽

Radial Basis Function ◽

Basis Function ◽

Clustering Algorithm ◽

Ordinary Least Squares ◽

Bankruptcy Prediction ◽

Data Sets ◽

Performance Metric ◽

Radial Basis ◽

Unsupervised Training

This chapter presents an algorithm to train radial basis function neural networks (RBFN) in a semi-online manner. It employs the online, evolving clustering algorithm of Kasabov and Song (2002) in the unsupervised training part of the RBFN and the ordinary least squares estimation technique for the supervised training part. Its effectiveness is demonstrated on two problems related to bankruptcy prediction in financial engineering. In all the cases, 10-fold cross validation was performed. The present algorithm, implemented in two variants, yielded more sensitivity compared to the multi layer perceptron trained by backpropagation (MLP) algorithm over all the problems studied. Based on the results, it can be inferred that the semi-online RBFN without linear terms is better than other neural network techniques. By taking the Area Under the ROC curve (AUC) as the performance metric, the proposed algorithms viz., semi-online RBFN with and without linear terms are compared with classifiers such as ANFIS, TreeNet, SVM, MLP, Linear RBF, RSES and Orthogonal RBF. Out of them TreeNet outperformed both the variants of the semi-online RBFN in both data sets considered here.

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