Detection and classification of buried dielectric anomalies using neural networks--further results Azimi-Sadjadi, Mahmood R. ; Stricker, Scott A. "This work was supported by the U.S. Army Belvoir RDandE Center under contract No. DAAL03-86-D-0001." The development of a neural network-based detection and classification system for use with buried dielectric anomalies is the main focus of this paper. Several methods of data representation are developed to study their effects on the trainability and generalization capabilities of the neural networks. The method of Karhonen-Loeve (KL) transform is used to extract energy dependent features and to reduce the dimensionality of the weight space of the original data set. To extract the shape-dependent features of the data, another data preprocessing method known as Zernike moments is also studied for its use in the detector/classifier system. The effects of different neural network paradigms, architectural variations, and selection of proper training data on detection and classification rates are studied. Simulation results for nylon and wood targets indicate superior performance when compared to conventional schemes. Colorado State University. Libraries 1994 text ; image application/pdf ECEmra00040.pdf FACFECEN100520ARTI eng c1994 IEEE
Detection and classification of buried dielectric anomalies using neural networks--further results
Azimi-Sadjadi, Mahmood R. ; Stricker, Scott A.
"This work was supported by the U.S. Army Belvoir RDandE Center under contract No. DAAL03-86-D-0001."
The development of a neural network-based detection and classification system for use with buried dielectric anomalies is the main focus of this paper. Several methods of data representation are developed to study their effects on the trainability and generalization capabilities of the neural networks. The method of Karhonen-Loeve (KL) transform is used to extract energy dependent features and to reduce the dimensionality of the weight space of the original data set. To extract the shape-dependent features of the data, another data preprocessing method known as Zernike moments is also studied for its use in the detector/classifier system. The effects of different neural network paradigms, architectural variations, and selection of proper training data on detection and classification rates are studied. Simulation results for nylon and wood targets indicate superior performance when compared to conventional schemes.
Colorado State University. Libraries
1994
text ; image
application/pdf
ECEmra00040.pdf
FACFECEN100520ARTI
eng
c1994 IEEE