Journal
APPLIED INTELLIGENCE
Volume 32, Issue 1, Pages 27-46Publisher
SPRINGER
DOI: 10.1007/s10489-008-0133-z
Keywords
Polynomial neural networks; Radial basis function neural networks; Pattern classification; Fuzzy clustering; Two-class discrimination
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Funding
- Korea government (MOST) [F01-2007-000-10058-0]
- Korea Evaluation Institute of Industrial Technology (KEIT) [KI001931] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Polynomial neural networks have been known to exhibit useful properties as classifiers and universal approximators. In this study, we introduce a concept of polynomial-based radial basis function neural networks (P-RBF NNs), present a design methodology and show the use of the networks in classification problems. From the conceptual standpoint, the classifiers of this form can be expressed as a collection of if-then rules. The proposed architecture uses two essential development mechanisms. Fuzzy clustering (Fuzzy C-Means, FCM) is aimed at the development of condition parts of the rules while the corresponding conclusions of the rules are formed by some polynomials. A detailed learning algorithm for the P-RBF NNs is developed. The proposed classifier is applied to two-class pattern classification problems. The performance of this classifier is contrasted with the results produced by the standard RBF neural networks. In addition, the experimental application covers a comparative analysis including several previous commonly encountered methods such as standard neural networks, SVM, SOM, PCA, LDA, C4.5, and decision trees. The experimental results reveal that the proposed approach comes with a simpler structure of the classifier and better prediction capabilities.
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