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Full-Text Articles in Engineering
Robust Partial Least-Squares Regression: A Modular Neural Network Approach, Thomas M. Mcdowall, Fredric M. Ham
Robust Partial Least-Squares Regression: A Modular Neural Network Approach, Thomas M. Mcdowall, Fredric M. Ham
Electrical Engineering and Computer Science Faculty Publications
We have developed a robust Partial Least-Squares Regression (PLSR) neural network approach to statistical calibration model development. Generalized neural network learning rules derived from a weighted statistical representation error criterion that grows less than quadratically are presented. This optimization criterion allows for higher-order statistics associated with the inputs to be taken into account and also serves to robustify the results when the empirical data contains impulsive and colored noise and outliers. The learning rules presented are considered generalized because they can be used to implement several specialized cases including: robust PLSR, linear PLSR, weighted least-squares, and variance scaling. The same …
Neural Network Approach To The Determination Of The Geophysical Model Function Of The Ers-1 C-Band Spaceborne Radar Scatterometer, Sami M. Alhumaidi, W. Linwood Jones
Neural Network Approach To The Determination Of The Geophysical Model Function Of The Ers-1 C-Band Spaceborne Radar Scatterometer, Sami M. Alhumaidi, W. Linwood Jones
Electrical Engineering and Computer Science Faculty Publications
Geophysical Model Functions (GMF) describing the relationship between the scatterometer normalized radar cross section (sigma-0) and useful geophysical parameters such as sea-surface wind vectors, wave heights, and sea- surface temperatures have been undergoing extensive research and development during the last decade. In this study, we investigate the use of two feed-forward neural networks, Multilayer Perceptron and Radial Basis Functions, for developing a useful and accurate representation of the C- band GMF. Collocated radar sigma-0 cells with global wind vector models were used as the database of the study. The resulting well-known biharmonic relationship between the sigma-0 and the relative azimuth …
Comparison Of Three Clustering Algorithms And An Application To Color Image Compression, Jihun Cha, Laurene V. Fausett
Comparison Of Three Clustering Algorithms And An Application To Color Image Compression, Jihun Cha, Laurene V. Fausett
Electrical Engineering and Computer Science Faculty Publications
This paper investigates a traditional clustering algorithm (K-means) and two neural networks (SOM and ART-F). The characteristics of each algorithm are illustrated by simulating geometric space data clustering. Then each algorithm is applied to image data sets to compress the size by reducing the number of colors from 256 to 16.