A Functional Link Network Using Ordered Basis Functions

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Title: A Functional Link Network Using Ordered Basis Functions
Author: Sureka, Saurabh
Abstract: A new function approximation and classification network based on Functional Link Network (FLN ) with orthonormal Polynomial Basis Functions (PBF ) is presented . By using an iterative Gram -Schmidt procedure , the PBF's are orthonormalized , ordered and selected based on their contribution to minimize the Mean Square Error (MSE ) . Linearly dependent and less useful PBF are detected and eliminated at an early stage thereby improving the approximation capabilities and reducing the possibility of combinatorial explosion . The number of passes through the data during network training is minimized through the use of correlations . A one -pass method is used for validation and network sizing . Equivalent function approximation and classification networks are designed and simulation examples are presented . Results for the Ordered FLN are compared with those for the FLN , Group Method of Data Handling (GMDH ) , and Multi -Layer Perceptron (MLP ) , Nearest Neighbor Classifier (NNC ) and Piecewise Linear Classifier (PLNC ) .
URI: http : / /hdl .handle .net /10106 /543
Date: 2007-09-17


A Functional Link Network Using Ordered Basis Functions. Available electronically from http : / /hdl .handle .net /10106 /543 .

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