The USC Andrew and Erna Viterbi School of Engineering USC Signal and Image Processing Institute USC Ming Hsieh Department of Electrical Engineering University of Southern California

Technical Report USC-SIPI-369

“The Choquet and Sugeno Fuzzy Integrals: A Tutorial”

by Arjun Bharadwaj and Jerry M. Mendel

December 2005

This report summarizes our research from Jan '05 to Dec '05. We have developed multi-category classifiers based on seismic data to classify heavy-tracked, heavy wheeled, light tracked and light-wheeled vehicles. We focused on data collected in the normal terrain.

We also developed fusion algorithms for type-1 and type-2 Fuzzy Logic Rule Based Classifiers (FLRBCs) based on the Choquet Fuzzy Integral (CFI). We conducted experiments to evaluate the performance of the classifiers and to evaluate the effectiveness of seismic data for classification. We also conducted experiments to evaluate the performances of the fused classifiers (both acoustic and seismic) and determine if performance could be improved. Our results show that binary classification between tracked and wheeled vehicles is effective using seismic data. However, due to the inherent unreliability of the seismic data, the performance of the classifiers based on seismic data was poor when compared to the performance of the classifiers based on acoustic data. Fusing the two classifiers also did not show any appreciable improvement in performance.

We note that FL-RBCs performed better than the Bayesian equivalent for all the experiments. This shows that FL_RBCs are better suited to handle uncertainties in the data.

To download the report in PDF format click here: USC-SIPI-369.pdf (8.0Mb)