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Proceedings Paper

Centralized and distributed hypothesis testing with structured adaptive networks and perceptron-type neural networks
Author(s): Stelios C.A. Thomopoulos; Ioannis Papadakis; Haralambos Sahinoglou; Nickens N. Okello
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Paper Abstract

Two different types of adaptive networks are considered for solving the centralized and distributed hypothesis testing problem. The performance of the two different types of networks is compared under different performance indices and training rules. It is shown that training rules based on the Neyman-Pearson criterion outperform error based training rules. Simulations are provided for data that are linearly and nonlinearly separable.

Paper Details

Date Published: 30 April 1992
PDF: 17 pages
Proc. SPIE 1611, Sensor Fusion IV: Control Paradigms and Data Structures, (30 April 1992); doi: 10.1117/12.57911
Show Author Affiliations
Stelios C.A. Thomopoulos, The Pennsylvania State Univ. (United States)
Ioannis Papadakis, The Pennsylvania State Univ. (United States)
Haralambos Sahinoglou, The Pennsylvania State Univ. (United States)
Nickens N. Okello, The Pennsylvania State Univ. (United States)


Published in SPIE Proceedings Vol. 1611:
Sensor Fusion IV: Control Paradigms and Data Structures
Paul S. Schenker, Editor(s)

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