RBF iterative construction algorithm (RICA)

Terry A. Wilson; Steven K. Rogers; Mark E. Oxley; Thomas F. Rathbun; Martin P. DeSimio; Matthew Kabrisky

doi:10.1117/12.304831

25 March 1998 RBF iterative construction algorithm (RICA)

Terry A. Wilson, Steven K. Rogers, Mark E. Oxley, Thomas F. Rathbun, Martin P. DeSimio, Matthew Kabrisky

Proceedings Volume 3390, Applications and Science of Computational Intelligence; (1998) https://doi.org/10.1117/12.304831
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

A Radial Basis Function (RBF) Iterative Construction Algorithm (RICA) is presented that autonomously determines the size of the network architecture needed to perform classification on a given data set. The algorithm uses a combination of a Gaussian goodness-of-fit measure and Mahalanobis distance clustering to calculate the number of hidden nodes needed and to estimate the parameters of the hidden node basis functions. An iterative minimum squared error reduction method is used to optimize the output layer weights. RICA is compared to several neural network algorithms, including a fixed architecture multilayer perceptron (MLP), a fixed architecture RBF, and an adaptive architecture MLP, using optical character recognition and infrared image data.

Citation Download Citation

Terry A. Wilson, Steven K. Rogers, Mark E. Oxley, Thomas F. Rathbun, Martin P. DeSimio, and Matthew Kabrisky "RBF iterative construction algorithm (RICA)", Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); https://doi.org/10.1117/12.304831

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