Azimi-Sadjadi, Mahmood R., authorStricker, Scott A., authorIEEE, publisher2007-01-032007-01-031994Azimi-Sadjadi, Mahmood R. and Scott A. Stricker, Detection and Classification of Buried Dielectric Anomalies Using Neural Networks--Further Results, IEEE Transactions on Instrumentation and Measurement 43, no. 1 (February 1994): 34-39.http://hdl.handle.net/10217/858The 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.born digitalarticleseng©1994 IEEE.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.data reductiondata structuresdielectric measurementdielectric properties of solidsdigital simulationfeature extractiongeophysical techniquesimage processing equipmentlearning (artificial intelligence)neural netspattern recognitionpolymerssoiltransformswoodText