Clustered-Hybrid Multilayer Perceptron network for pattern recognition application

Authors:
Nor Ashidi Mat Isa;Wan Mohd Fahmi Wan Mamat
Affiliations:
Imaging and Intelligent System Research Team (ISRT), School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia;Imaging and Intelligent System Research Team (ISRT), School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia
Venue:
Applied Soft Computing
Year:
2011

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Abstract

This paper introduces a modified version of the Hybrid Multilayer Perceptron (HMLP) network to improve the performance of the conventional HMLP network. We adopted the Clustering Algorithm from the Radial Basis Function (RBF) network architecture and incorporated it into the conventional HMLP network architecture. The modified model is called Clustered-Hybrid Multilayer Perceptron (Clustered-HMLP) network. The proposed Clustered-HMLP network architecture is trained using modified training algorithm called Clustered-Modified Recursive Prediction Error (Clustered-MRPE). The capability of the Clustered-HMLP network with Clustered-MRPE training algorithm is demonstrated using seven benchmark datasets from the University of California at Irvine (UCI) machine learning repository (i.e. Iris, Ionosphere, Pima Indian Diabetes, Wine, Lung Cancer, Hayes-Roth and Glass) and compared with the performance of other twelve classifiers reported in literature. Further, the new network is implemented to model a Transformer Fault Diagnosis System and Aggregate Shape Identification System. The results indicate that the proposed Clustered-HMLP network outperforms other eleven classifiers and provides a significant improvement to the conventional HMLP network for pattern recognition application.